# Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights

Maryam Ben Driss∙, Essaid Sabir∙, Halima Elbiaze∙ , Walid Saad∗,

∘Department of Computer Science, University of Quebec at Montreal (UQAM),
Montreal, H2L 2C4, Canada  ∗WirelessVT, Bradley Department of Electrical and
Computer Engineering, Virginia Tech, Arlington, VA, USA

(September 20th, 2023)

###### Abstract

Artificial Intelligence (AI) is expected to play an instrumental role in the
next generation of wireless systems, such as sixth-generation (6G) mobile
network. However, massive data, energy consumption, training complexity, and
sensitive data protection in wireless systems are all crucial challenges that
must be addressed for training AI models and gathering intelligence and
knowledge from distributed devices. Federated Learning (FL) is a recent
framework that has emerged as a promising approach for multiple learning
agents to build an accurate and robust machine learning models without sharing
raw data. By allowing mobile handsets and devices to collaboratively learn a
global model without explicit sharing of training data, FL exhibits high
privacy and efficient spectrum utilization. While there are a lot of survey
papers exploring FL paradigms and usability in 6G privacy, none of them has
clearly addressed how FL can be used to improve the protocol stack and
wireless operations. The main goal of this survey is to provide a
comprehensive overview on FL usability to enhance mobile services and enable
smart ecosystems to support novel use-cases. This paper examines the added-
value of implementing FL throughout all levels of the protocol stack.
Furthermore, it presents important FL applications, addresses hot topics,
provides valuable insights and explicits guidance for future research and
developments. Our concluding remarks aim to leverage the synergy between FL
and future 6G, while highlighting FL’s potential to revolutionize wireless
industry and sustain the development of cutting-edge mobile services.

###### Index Terms:

Artificial Intelligence; Federated Learning; Decentralized Machine Learning;
Privacy; Physical Layer; MAC Layer; Network Layer; Transport Layer;
Application Layer; Cellular Networks; 6G.

##  I Introduction

###  I-A Artificial Intelligence for Wireless

Artificial Intelligence (AI) is expected to play a prominent role in current
and future wireless systems, such as fifth-generation (5G) and sixth-
generation (6G) [1, 2]. Indeed, 6G systems are envisioned to be AI-native
systems in which some form of AI will be implemented for automation across the
protocol stack [3, 4, 5]. For instance, AI is expected to provide new
frameworks for improving wireless performance metrics, including capacity,
latency, efficiency, power, spectrum frequency, flexibility, compatibility,
and quality of experience [6]. AI techniques will influence next-generation
network systems, due to two key reasons:

  * •

_Need for more autonomy:_ Machine Learning (ML) techniques are necessary to
endow wireless systems with autonomy and self-dependence by analyzing real-
time data, such as traffic patterns, user behavior, and environmental factors,
to make intelligent decisions for optimizing network resources, routing, and
load balancing. This level of dynamic network management allows wireless
systems to adapt to changing conditions without manual intervention.

  * •

_Need for more communication:_ AI systems, with their ability to process vast
amounts of data and learn from interactions, significantly contribute to
making communication more accessible, efficient, and personalized to meet the
diverse and growing demands of modern users and applications.

Numerous recent research activities have been focused on surveying the core
wireless networking issues [7] that are addressed using various ML techniques
such as spectrum management and sharing [8, 9, 10, 11, 12, 13], resource
allocation [14, 15, 16, 17, 18, 19], fading channels prediction [20, 21, 22,
23, 24], and traffic prediction [25, 26, 27, 28]. However, applying
traditional ML schemes in large-scale systems is still challenging [29][30],
for the following reasons (non-exhaustive list):

  * •

_Scalability:_ Large-scale systems often handle massive amounts of data and
require complex computations. In a centralized approach, all data is collected
and processed on a single server, resulting in performance bottlenecks and
increased processing times.

  * •

_Communication overhead:_ In a centralized scheme, data from various sources
must be transmitted to the central server for processing. This leads to
significant communication overhead, especially when dealing with
geographically distributed data sources.

  * •

_Single point of failure:_ Centralized systems have a single point of failure.
Any failure of the server will impact the entire ML process. This
vulnerability is critical for real-time or mission-critical applications that
require continuous and reliable operation.

  * •

_Privacy and security concerns:_ Centralized ML involves collecting and
storing data from multiple sources in a central location. This raises concerns
about privacy and security, as sensitive data are at risk without proper
security measures in place.

  * •

_Latency and real-time requirements:_ For real-time or low-latency
applications, a centralized approach is not feasible due to the long delay of
processing and transmission to the central server.

  * •

_Cost and infrastructure:_ Building and maintaining a centralized ML
infrastructure for large-scale systems is costly. It requires substantial
computing power, storage, and network resources to handle the volume of data
and computation involved.

To address these limitations, the concept of federated learning (FL) has been
recently proposed, as an effective approach to train an ML model in a
distributed way [31]. FL allows multiple devices or nodes to collaboratively
train a global model without sharing local data. In traditional ML, data is
collected from various sources, sent to a centralized server, and then used to
train a global model. However, the training process of FL occurs directly on
individual devices or nodes without sharing raw data. FL framework offers a
promising approach for future wireless systems by combining the benefits of
distributed processing, privacy preservation, scalability, and adaptability.
It empowers the evolution of wireless communication and enables the
development of innovative and data-driven applications more efficiently and
securely. Still, there are several limitations to implementing FL due to the
distributed and dynamic nature of wireless networks. Some of the key
challenges include:

  * •

_Limited bandwidth:_ Transmitting model updates between the central server and
edge devices is resource-intensive and leads to increased communication
overhead.

  * •

_Unreliable connectivity:_ Managing the participation of devices with varying
connectivity and capabilities requires robust mechanisms for synchronization
and communication.

  * •

_Computing constraints:_ Participating clients are usually equipped with
limited computational capabilities, which may affect the convergence speed and
whole model performance.

  * •

_Imbalance and non-IID data:_ In wireless networks, the data collected by edge
devices is generally imbalanced or not independent and identically distributed
(non-IID), which presents challenges for model convergence and generalization.

Addressing these challenges requires a careful combination of algorithmic
advancements, optimization techniques, and architectural design to make FL
feasible and effective in wireless environments. As the field of FL evolves,
researchers continue to work on overcoming these challenges to enable
practical and secure deployments in future wireless networks.

###  I-B Relevant Surveys and our Contribution

Figure 1: The survey paper organization.

Although there are many surveys on FL and wireless networks [32, 33, 34, 35,
36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 45, 46, 47, 48, 49, 50, 51, 52, 53,
54] as summarized in Table I, they do not address their applicability across
the protocol stack or provide a detailed examination of FL applications in
each layer of the Open Systems Interconnection (OSI) model. This survey aims
to fill this gap by providing an in-depth overview of how FL is implemented
throughout the entire protocol stack. We also highlight associated advantages
and discuss induced challenges. State-of-the-art contributions that utilize
federated ML schemes to address problems in wireless communications are
collected and discussed. Our main contributions include:

  1. 1.

Drawing a comprehensive background on recent FL paradigms, taxonomy and main
techniques;

  2. 2.

Comparing centralized ML, distributed ML, and FL in the context of massive and
sensitive environments while highlighting the characteristics and the issues
associated with each approach;

  3. 3.

Exploring the latest FL schemes aiming to solve the major issues at the
physical (PHY), medium access control (MAC), network (NET), transport, and
application (APP) layers while achieving high accuracy, enhanced communication
efficiency and low energy consumption. We also draw a detailed analysis of the
benefits and drawbacks of each application/solution;

  4. 4.

Discussing relevant insights and open issues related to the application of FL
in 6G and future mobile networks.

For a pleasant reading experience, our survey is organized as depicted in Fig.
1. Section II presents ML architectures and their challenges in the context of
wireless systems. In Section III, we present a comprehensive overview of FL
fundamentals, features, taxonomy, frameworks, benefits, and issues. Next, we
explore FL for wireless operations, while focusing on PHY/MAC, NET and
Transport/APP layers, in Section IV, Section V and Section VI, respectively.
Next, we present recent FL verticals and applications in Section VII. Then,
Section VIII summarizes the lessons learned and provide a detailed discussion
of FL-wireless open problems that need to be addressed for successful
integration in 6G and future systems. Finally, we highlight some promising
directions for future research towards efficient AI-native networking.

Ref | Year | Target application | Basics | PHY Layer | NET layer | Transport Layer | APP Layer | Challenges
---|---|---|---|---|---|---|---|---
[32] | 2020 | FL in mobile edge networks |  |  |  |  |  |
[33] | 2020 | FL for wireless communications |  |  |  |  |  |
[34] | 2020 | Distributed ML for communication networks |  |  |  |  |  |
[35] | 2020 | FL and wireless communications |  |  |  |  |  |
[36] | 2021 | ML and FL for handover management in 5G |  |  |  |  |  |
[37] | 2021 | Federated machine learning for 6G |  |  |  |  |  |
[38] | 2021 | FL for physical layer |  |  |  |  |  |
[39] | 2021 | FL in networking systems |  |  |  |  |  |
[40] | 2021 | Federated learning for IoT |  |  |  |  |  |
[41] | 2021 | Recent advances of FL for IoT networks |  |  |  |  |  |
[42] | 2021 | Federated ML for big data |  |  |  |  |  |
[43] | 2021 | Intelligence for 6G using FL |  |  |  |  |  |
[44] | 2021 | The internet of federated things |  |  |  |  |  |
[45] | 2022 | From distributed ML to FL |  |  |  |  |  |
[46] | 2022 | FL and next-generation communications |  |  |  |  |  |
[47] | 2022 | FL attacks and threats |  |  |  |  |  |
[48] | 2022 | Federated learning fundamentals |  |  |  |  |  |
[49] | 2022 | FL for intrusion detection |  |  |  |  |  |
[50] | 2022 | FL for IoT and cybersecurity |  |  |  |  |  |
[51] | 2022 | The application of FL in smart cities |  |  |  |  |  |
[52] | 2023 | FL challenges and applications |  |  |  |  |  |
[53] | 2023 | Trends and challenges of FL for 6G |  |  |  |  |  |
[54] | 2023 | Communication efficiency in FL |  |  |  |  |  |
Our work | 2023 | Multilayered survey on FL for B5G and 6G |  |  |  |  |  |
TABLE I: Related existing survey papers on FL for network communications.

##  II Machine Learning Architectures

Here, we provide an overview of ML architectures including traditional
centralized learning, distributed learning and FL.

###  II-A Centralized Machine Learning

ML is the science of enabling computers to learn without being explicitly
programmed. This branch of AI allows systems to identify patterns in data,
make decisions, and predict future outcomes [55, 56]. Standard ML techniques
require data to be stored on a single machine for processing and model
training (Fig. 2.1). Today, Internet of Things (IoT) applications use
traditional ML [57] by uploading data from IoT sensors to the cloud. The cloud
server trains a global model using data from multiple devices, allowing
immediate interaction with other participants [58]. However, centralized ML
suffers from numerous drawbacks, especially in wireless environments [59].
Hereafter, we provide a non-exhaustive list of these limitations:

  * •

_Privacy issues:_ Centralized learning involves collecting and storing
sensitive data from various sources, raising significant privacy concerns as a
potential target for unauthorized access [60].

  * •

_Latency issues:_ In centralized learning, data from individual devices is
transmitted to a central server. This data transfer introduces latency, which
is highly undesirable in applications like real-time communications,
autonomous vehicles, or industrial automation, where low latency is critical.

  * •

_Costs:_ Maintaining and operating centralized servers with the computational
power and storage capacity required for large-scale ML is expensive for
network operators [61].

It is worth noting that decentralized approaches, such as edge ML, have been
designed to address some of the challenges of centralized ML techniques.

Figure 2: Centralized ML has to store data in one data center. Decentralized
ML distributes the model across connected devices.

###  II-B Decentralized Machine Learning

The concept of decentralized ML was proposed (See [62] and [63]) as an
effective solution to address the key challenges listed in Section II.A. As
shown in Fig. 2.2, the decentralized ML approach distributes the computational
and data processing tasks across multiple decentralized devices rather than
relying on a central server [64] which offers several benefits, such as:

  * •

Simplifying learning since each device needs only its dataset while sharing
minimal information with external participants.

  * •

Adapting to changes over time and learning without being limited by an
internet connection or reliance on a central device.

  * •

Allowing sensitive data to remain on local devices or edge servers without
transmitting to a central location. This enhances data privacy and security.

Overall, edge learning empowers wireless communication systems to efficiently
handle data, improve response times, enhance privacy, and create more
resilient and scalable applications, making it a valuable technology for the
growing IoT and 5G ecosystems. Nevertheless, various challenges limit the
generated local models to benefit from peer data and learn from external
experiences, such as:

  * •

_Data heterogeneity:_ In large-scale environments, data collected from
different sources has diverse distributions, leading to challenges in
combining and aggregating the models effectively.

  * •

_Communication overhead:_ The exchange of model updates among distributed
nodes introduces communication overhead, particularly in bandwidth-constrained
networks.

  * •

_Synchronization:_ Coordinating the timing of model updates across distributed
nodes is complex, leading to synchronization issues and potential
inconsistencies.

Figure 3: FL: Collaborative ML without centralized training data.

FL offers a privacy-preserving, scalable, and efficient approach that
addresses many of the challenges associated with traditional centralized and
decentralized ML, making it a valuable solution in the era of data privacy and
decentralization. It is particularly well-suited for applications involving
sensitive data, edge computing, and distributed environments.

###  II-C Federated Machine Learning

FL was recently proposed by Google [65] as a promising approach for performing
distributed ML tasks without relying on a centralized data center. The authors
in [66] defined FL as an ML setting wherein multiple entities (clients) work
together to solve an ML problem under the coordination of a central server or
service provider. In this approach, personal data remains stored locally and
is not exchanged or transferred between clients (e.g., see Fig.3). Instead,
the model updates designed for aggregation are used to achieve the learning
objective, enabling secure training while leveraging the collective knowledge
of the distributed entities. FL is based on the idea of training a model
locally at the data source. The devices communicate their models by combining
partial training results into a new supermodel, which is then sent back to all
devices [67]. This mechanism is strategically designed to protect privacy and
enhance data security [68]. Fig 4 illustrates that FL depends on the
collaboration of ML, privacy, and distributed systems expertise which is
fundamental to addressing the challenges posed by distributed data and privacy
concerns while harnessing the collective intelligence of decentralized
entities for improved model performance and scalability.

Figure 4: FL encompasses three synergistic research areas: ML, privacy, and
distributed systems.

##  III Federated Learning Fundamentals

Before discussing FL applications in wireless systems, it is necessary to
provide a brief overview of FL concepts, taxonomy, and techniques. The main
concepts of FL are summarized in Table II.

Goal |  Distributed model training without centralized data collection and with privacy guarantees
---|---
Setting |  $K$ devices, out of which $C$ are selected ($C\leq$K)
Parameters |  • Batch size • Number of clients • Local iterations
Orchestration |  A central orchestration server or service organizes the training but never sees raw data.
Advantages |  • Respecting user’s privacy • Minimum hardware required • Saving user’s resources • Working offline • Computing in real-time • Decreasing training complexity
TABLE II: FL concepts.

###  III-A Federated Learning Life Cycle

The FL process is typically driven by an engineer developing a model for a
specific application [66]. In Fig. 5, the FL workflow generally comprises four
steps that occur sequentially.

  * •

_Model selection:_ The global model and parameters are initiated on a central
server and shared with all participants.

  * •

_Local model training:_ Clients train the model locally using their data
without sharing it with the central server or other clients.

  * •

_Aggregation of local models:_ The update is sent to the central server to
aggregate received parameters and create a new global model.

  * •

_Distribution of global model:_ The distribution of the global model among
participants for the next iteration.

###  III-B Different Flavors of FL

FL frameworks are classified into three types: horizontal, vertical, and
federated transfer learning, based on the distribution of data samples and
features [69, 70, 34].

####  III-B1 Horizontal Federated Learning

Horizontal FL (HFL) or sample-based FL is applied to scenarios in which
datasets share the same feature space but differ in the sample [69]. In other
words, the participating client devices share the same features but target
different populations. considering two banks operating in the same country,
the clients of those banks are non-overlapping, their data are likely to have
a similar feature space since they adopt similar businesses and operate in the
same country. Partitioning by samples is usually relevant when a single
company cannot centralize its data due to legal constraints or when
organizations with similar aims want to improve their models collaboratively
[71]. Wake-word recognition [72], such as ”Hey Siri” and ”OK Google”, is a
typical application of horizontal partition since each user says the same
sentence in a different accent.

Figure 5: FL Life Cycle.

####  III-B2 Vertical Federated Learning

Vertical FL (VFL) is specifically applicable in scenarios where two datasets
share the same sample space but differ in their feature space [69]. To
illustrate this FL category, consider two companies located in the same town,
one being a bank, and the other an e-commerce company. While their user base
is overlapping, their data collections diverge. The bank tracks user sales,
spending activity, and credit scores, while the e-commerce store captures user
search and buying history. As the objective is to train a model predicting
product purchases based on user features, VFL emerges as the ideal solution to
gather additional features and construct a comprehensive model using data from
both companies.

####  III-B3 Federated Transfer Learning

Federated transfer learning (FTL) is suitable where two datasets vary in terms
of samples and features space [73]. Considering two companies from two
countries whose users and feature spaces have a small intersection. In this
scenario, FTL learns from the common instances between the spaces using the
limited standard sample sets. The knowledge gained is later applied to make
predictions for samples with only one-sided features. In wireless systems, FTL
enables devices to remember what they have learned and what other devices have
learned [74] and this is applicable to cases in which multiple wireless
devices make decisions in different environments.

####  III-B4 Other Categories

There are other FL architectures, beyond HFL, VFL, and FTL. For instance,
multi-participant multi-class VFL (MMVFL) was proposed in [75] to manage many
users. It allows privacy-preserving label distribution from its owner to other
participants. FEDF is another framework introduced in [76] to train ML models
on multiple geographically distributed training datasets belonging to several
owners. Another FL framework, called PerFit, was developed in [77] to enhance
the performance of devices in IoT applications by learning a shared model
through aggregating local updates from distributed devices and utilizing the
benefits of edge computing. This approach enables personalization and improved
performance for IoT devices. In general, there are several architectures for
FL depending on the distribution characteristics of the data (e.g., see Table
III).

Ref | Architecture | Focuses | Benefits | Drawbacks
---|---|---|---|---
[69] | HFL | Security | Independence | Need to provide more security
[69] | VFL | Privacy | Encryption | Handling only two participants
[73] | FTL | Avoiding accuracy loss | High precision | Expensive computation
[75] | MMVFL | Data leakage | Multiple participants | Need to handle stragglers effectively
[76] | FEDF | Parallel training | Accelerating training | Enormous data exchange
[77] | PerFit | IOT applicability | Cloud based | Data augmentation step is required
[78] | StarFL | Urban computing | Security | System efficiency
TABLE III: FL architectures: focuses, benefits and drawbacks.

Furthermore, the hybrid federated centralized learning (HFCL) approach
proposed in [79] contributes to the learning process with their datasets. In
cases where certain edge devices lack the computational capacity to compute
gradients during model training, HFCL proves valuable because it exclusively
enables active devices with sufficient computing capabilities to conduct
gradient computations on their respective local datasets and engage in
collaborative training of the learning model. In contrast, the remaining
passive devices send their local datasets to the parameter server. In [80],
the authors proposed a novel concept of collaborative FL, whereby devices
leverage FL techniques without relying on a centralized parameter server. The
work in [80] showcases the architecture and challenges of this CFL paradigm.

###  III-C Federated Learning Algorithms

In FL, an ML model is learned iteratively by repeating the following steps: 1)
The server chooses a set of users to compute an updated model; 2) Each client
computes a locally trained model to update its local data; 3) Updates are
transferred to the server; and 4) The server aggregates these local updates to
construct a global model. The computation happens on the user’s devices and
the central aggregator, while contact occurs between them [81]. The goal of
computation is the model preparation, and the communication usually aims to
share model parameters [82]. In this section, we present important FL
algorithms (Table IV).

#### FedAvg

Federated averaging (FedAvg) is a popular algorithm that uses an iterative
averaging process to train the global model [83]. It divides the data into
multiple non-overlapping subsets, and each client trains a local model on its
subset. The local models are then sent to the server, and their parameters are
averaged to create a new global model. This process is repeated for a fixed
number of rounds until the global model converges. The steps of FedAvg are
almost identical to traditional ML and deep learning (DL) techniques with a
parameter-server and workers [83].

#### FedSGD

The federated stochastic gradient descent is similar to FedAvg, devices use
stochastic gradient descent to update models locally, and each device adjusts
its learning rate, enabling better convergence for devices with different data
distributions.

#### FedProx

In heterogeneous networks, federated optimization incorporates a proximal term
to link the local and global models, to minimize excessive drift during the
optimization process. FedProx modifies the FedAvg algorithm by enabling
partial work to be conducted across devices based on the underlying system
restrictions and utilizing the proximal term to securely include the partial
work [84].

#### FedATT

Attentive federated examines the relative significance of local models and
aggregates them using a layer-wise soft attention mechanism between local and
global models. This attentive aggregation method minimizes the weighted
distance between the server and client models on non-IID datasets [85].

#### SimFL

Similarity-based FL (SimFL) represents an FL framework where no trusted server
is needed [86]. It includes four main steps:

  1. 1.

The parties first update the gradients of their local data.

  2. 2.

The gradients are sent to a selected party.

  3. 3.

The selected party uses its local data and gradients to update the model.

  4. 4.

The model is sent to all the other parties.

To promote fairness and effectively leverage data from multiple parties, each
participating party is chosen to update the model for a similar number of
rounds and outputs the final model.

Various FL frameworks are designed to facilitate the implementation and
management of the FL process. These frameworks provide tools, libraries, and
APIs that enable developers to create and deploy FL algorithms:

#### TFF

The tensorflow federated is an open-source framework for ML and other
computations on decentralized data. It has been developed to facilitate open
research and experimentation with FL.

#### PySyft

A privacy-preserving FL framework built over PyTorch was introduced in [87].
The design relies on chains of tensors exchanged between local and remote
workers.

#### LEAF

LEAF was proposed in [88]; it is a modular framework for learning in federated
settings or ecosystems marked by massively distributed networks of devices.

#### Paddle FL

An open-source framework designed mainly for industrial applications [89]
which effectively replicates several FL algorithms for large-scale distributed
clusters.

#### FATE

The federated AI technology enabler is an open-source platform that aims to
create a safe computing environment for the federated AI ecosystem [90].

#### IBM FL

A python-based FL framework proposed by IBM for enterprise environments, which
provides an essential fabric for adding advanced features [91].

#### FedML

An open research library proposed in [92] to support the development of FL
algorithms and fair performance comparison. It offers three computing
paradigms: edge device on-device training, distributed computing, and single-
machine simulation.

Algorithm |  Benefits |  Drawbacks
---|---|---
FedAvg |  \- Simplicity \- Communication-efficiency \- Privacy-preserving |  \- Slow convergence speed \- Privacy risks \- Data heterogeneity
FedSGD |  \- High convergence speed \- Handling data heterogeneity \- Localized Adaptation |  \- Hyper-parameter complexity \- Communication overhead \- Impact of imbalanced data
FedProx |  \- High convergence speed \- Handling non-IID data \- Regularization |  \- Hyper-parameter tuning \- Increased communication overhead \- Centralized aggregator
FedATT |  \- Fast learning \- Communication efficiency \- Handling non-IID data |  \- Privacy risks \- Data heterogeneity
SimFL |  \- Good accuracy \- Fast computation \- Security |  \- Communication overhead \- Impact of imbalanced data
TABLE IV: FL algorithms: benefits and drawbacks.

In this section, we provided a taxonomy of the essentials of FL, including a
concise definition of FL, its various categories, and the associated
algorithms. Moving forward, we will delve into FL applications and explore how
they are leveraged to enhance the capabilities of the 5G and 6G mobile
generations.

##  IV Federated Learning at PHY/MAC layers

With the promising performance of ML in wireless communication [35], there has
been a surge in the area of ML for the wireless physical layer e.g., see [93]
and [94]. FL is more communication efficient and privacy-preserving than
traditional ML and, thus, it has a promising potential for addressing problems
at the lowest layers of the OSI model [38]. This section provides an overview
of recent advancements in FL-based physical layer architecture, including
symbol and signal detection, channel estimation, channel state information,
beamforming, reconfigurable intelligent surfaces, channel access, and spectrum
sharing.

###  IV-A Symbol/Signal Detection

The receiver in a digital communication system must correctly identify the
transmitted symbols from the observed channel output. This task is known as
symbol detection [95]. The key benefit of using ML and DL techniques for
symbol detection is to provide a data-driven mapping for modeling channel
characteristics that model-based techniques do not effectively manage [96]. In
addition, since the model is directly fed with the obtained corrupted symbols,
end-to-end learning allows the model to detect the symbols correctly without a
channel estimation stage [93]. Using centralized learning for this role
introduces high communication overhead and privacy issues, as well as
limitations related to storing and transmitting a large amount of data.

To overcome these challenges, the work in [97] designed an FL-based receiver
(FedRec) for symbol detection in downlink fading channels, designed to learn
its mappings from a limited number of pilots. This approach takes advantage of
this intuition by enabling users to collaborate for training through FL, which
allows a single neural network to be trained over a diverse data set without
additional pilots. Despite the need for numerous iterations of parameter
exchanges with the base station (BS), FedRec yields an efficient symbol
detector that achieves performance levels close to a MAP detector. This
outperforms the model-based solution, especially when faced with inaccurate
knowledge of the fading distribution. Furthermore, FedRec induces
substantially less communication overhead than learning a neural network (NN)
based symbol detector in a centralized scheme. FedRec facilitates utilizing
compact NNs by avoiding feature extraction layers, which are trainable using
relatively small datasets. While each local training data set encapsulates a
relatively small number of fading channel realizations, the diversity among
these data sets at different devices is exploited to obtain a unified model
for all the devices by training in a federated manner.

The advantage of FL is recognizing signal modulation while protecting private
data. As such, in [98], instead of transmitting the raw data between the
central server and all devices, only the convolutional NN (CNN) model updates
are shared. The results show that the designed approach has reached more than
70% recognition rate while satisfying privacy protection and data security.

The authors of [99] presented an FL framework for automatic modulation
classification named FedeAMC; under conditions of class imbalance and noise
varying, data and training are in each local client, while only knowledge is
shared with the server. The FL process contains six main steps: model and
parameters choosing and broadcasting, local gradient calculation, key
information uploading, key information aggregation, aggregated information
downloading, and finally local model updating. The proposed approach achieves
a low risk of data leakage without severe performance loss.

###  IV-B Channel Estimation

In wireless communication, there are some undesirable effects on the signals
transmitted over the wireless channel caused by the physical properties of the
channel. As a result, the signals arriving at the receiver end of the
communication system are always attenuated, distorted, delayed, and phase-
shifted [100]. Consequently, there is a need to provide a perfect and up-to-
date estimation of the channel to compensate for these effects, and accurate
signal demodulation, equalization, and decoding at the receiver end of the
systems. Channel estimation through ML [101] requires model training on a data
set in a centralized manner, which typically includes received pilot signals
as input and channel data as output [102]. However, this method generates
significant transmission overhead when collecting user data. To deal with this
problem, FL was considered in [103] to enhance the performance of channel
estimation; instead of sending the entire dataset, only the model updates are
transmitted between clients and server, which maintains good channel
estimation and reduces the estimation error, as well as the transmission
overhead that approximately 16 times lower than central learning. The authors
designed a convolutional neural network at the BS and trained on the local
data sets. The proposed approach has three stages:

  1. 1.

_Data collection:_ Each user creates its local training dataset, which
contains the input (signals pilot) and output (channel matrix) for model
training.

  2. 2.

_Training:_ Each user works with its local dataset, computes model changes and
sends them to the BS, where they are aggregated to train a global model.

  3. 3.

_Prediction:_ Each user will estimate its local channel by simply feeding the
obtained pilot data into the trained model.

The fact that each client has access to the qualified model for channel
estimation is a significant benefit of this method.

The authors of [104] used FL to create a channel prediction scheme to achieve
channel pre-compensation for a low-cost free-space optical communication
system. The proposed approach is effective in simplifying system structural
and operational costs.

###  IV-C Channel State Information

To reap the full benefits of the reconfigurable intelligent surfaces (RIS)
architecture. All efficient technologies must rely on the perfect channel
state information (CSI) between the BS and the RIS and the perfect CSI between
the RIS and users [105]. However, it is infeasible for RIS-enhanced systems to
estimate correct CSI when radio frequency (RF) sensors or chains are not
installed on the RIS. Therefore, employing FL for CSI detection in RIS-
assisted wireless communications makes sense. In fact, for some small cellular
BSs, a small amount of training data is not sufficient to produce a
generalized model for CSI prediction. Centralized learning techniques combine
all the data for processing and training, which results in communication
overhead [106].

The authors in [107] introduced a decentralized approach to overcome these
challenges. They proposed an FL-based framework for CSI prediction using
three-dimensional convolutional neural networks. The global model is trained
at macro BSs (MBs), by gathering all local datasets from the edge BSs. To deal
with the performance gap between CL and FL, they designed the federated
weights and gradients (FED-WG) algorithm, which works as follows:

  1. 1.

The MBs get the relevant local model weights and gradients from the BSs.

  2. 2.

FED-WG executes two consecutive rounds of parameter updates for the global
model.

The suggested FED-WG framework effectively narrows the performance gap between
FL and CL, while significantly reducing the transmission overheads.

The general process of FL approaches typically assumes perfect knowledge of
the CSI during the training phase, which is challenging for fast-fading
channels. In addition, the literature uses a fixed number of clients to
participate in the federated model’s training. The work in [108] tried to fill
these gaps by proposing an FL approach where all clients use a constant global
rate to complete training rounds. This consistently results in faster
convergence, even when the CSI is imperfect.

###  IV-D Beamforming

Beamforming and massive multiple input multiple outputs (MIMO) systems aim to
adapt the radiation pattern of an antenna array to particular scenarios. These
technologies are a crucial part of 5G and 6G new radio (e.g., see [37] and
[109]). Using a centralized learning scheme for hybrid beamforming has been
widely studied. Incorporating FL into beamforming and massive MIMO systems
holds the potential to enhance network efficiency, robustness, and
scalability. As 5G and 6G technologies continue to evolve, integrating
advanced learning techniques plays a pivotal role in shaping the future of
wireless communication.

The authors in [110] provide an FL-based framework to map the millimeter wave
(mmWave) channels into analog beamformers in a multi-user downlink network.
They built a CNN architecture at the BS, where the model is trained on the
edge using only the gradients provided by the different users. The approach of
[110] proceeds as follows:

  1. 1.

The CNN model takes the channel matrix as input and the RF beamformer as
output. The deep neural network is then trained by using the gradient data
collected from the users.

  2. 2.

Each user computes the gradient information with its available training data
(a pair of channel matrices and corresponding beamformer index) and then sends
it to the BS.

  3. 3.

The BS receives all the gradient data from the users and performs the
parameter updates for the CNN model which is comprised of 11 layers with two
convolutional layers and a single fully connected layer. As a result, the
beamformer indices are the CNN’s output.

FL strategy for hybrid beamforming provides more robust beamforming
performance while exhibiting much less transmission overhead.

This framework requires the mmWave channel matrices as inputs, which is
challenging to estimate and requires more significant training overheads. The
authors in [111] suggested a beamforming scheme relies only on the channel
state estimation at sub-6GHz, which is considerably more accessible with
current technologies compared to mmWave channel estimation. The authors aim to
predict the mmWave beamforming vectors exploiting sub-6GHz channels for a
network composed of multiple access point–user links, where the deep neural
network accepts the sub-6GHz channels as input and outputs directly the
corresponding mmWave beamforming vector. Moreover, the authors suggest a
distributed FL scheme to predict the beamforming vectors locally at each user
without uploading the local data to a central server. There are three main
benefits of this framework:

  * •

The computation load is distributed to the edge of the network as opposed to a
centralized cloud-based approach;

  * •

Users only share their neural network parameters and not their data, which
highly reduces the signaling load, and protects the data of the individual
users;

  * •

Users still share their acquired knowledge of the environment to improve the
quality of their predictions.

mmWave-MIMO communication systems substantially improve the throughput in 5G
networks [112]. However, these solutions reduce the multiplexing gain in the
case of a small number of RF chains. The work in [113] introduced spatial path
index modulation (SPIM), a technique for improving gain using other signal
bits modulated by spatial path indices. The authors proposed model-based and
model-free frameworks for beamformer design in multi-user SPIM-MIMO systems;
they first designed the beamformers via a model-based manifold optimization
algorithm. Then, the authors train a CNN model on the local dataset using FL
and dropout learning to mitigate overfitting. The model updates are then
gathered at the BS for aggregation and then sent back to the users for the
prediction phase, where the model estimates beamformers by feeding them their
channel data. The advantages of utilizing dropout learning in FL include:

  1. A

Achieving approximately 10 times lower overhead than centralized learning.

  2. B

Reducing the communication cost during training.

  3. C

Achieving superior spectral efficiency compared to model-based SPIM and
conventional mmWave-MIMO.

###  IV-E Reconfigurable Intelligent Surfaces

Reconfigurable Intelligent Surfaces (RIS) have emerged as a strong contender
in comparison to massive MIMO. This technology has garnered notable research
interest [1], particularly in harnessing recent Machine Learning techniques,
especially FL, to enable reconfigurable propagation settings in wireless
communication capabilities. The integration of RIS enhances the beamforming
gain in uplink communications within a massive MIMO system [114]. Nonetheless,
this approach introduces several challenges such as ignoring user privacy and
producing high communication overhead.

To overcome these challenges, the study outlined in [115] introduced an FL-
driven beamformer design tailored for an IRS-assisted scenario. This entails
an algorithm referred to as ”optimal beam reflection based on FL” aiming to
enhance high-speed communication with sparse channel state information (CSI)
by improving the data rate and protecting privacy. The standard FL algorithm
is adopted to train a regression multi-layer perceptron with model
transmission; the proposed framework effectively reaches a theoretical value
that exceeds 90% of what is achieved by centralized Machine Learning, all
while ensuring user privacy protection. Nevertheless, the RIS-user link is the
only channel that is designed, while the BS-RIS link is supposed to be
constant. However, in a practical case, the mm-Wave channel tends to be highly
complex and characterized by a limited channel length, largely owing to
environmental fluctuations. Hence, the solution of [103] estimates both direct
(BS-user) and cascaded (BS-IRS-user) channels in an RIS-assisted scenario,
where input and output data are combined for each communication link only a
single CNN architecture is designed instead of different NNs.

A privacy-preserving paradigm combining FL with RIS in the mmWave
communication systems is designed in [116]. First, the private data handled on
each local device trains and encrypts the local models. Then, at the central
server, a global model is created by aggregating them. While ensuring user
data confidentiality, the suggested technique successfully approaches
theoretical value and achieves greater than 95% of that created by centralized
ML.

###  IV-F Channel Access

ML and DL techniques have significantly enhanced the design of the MAC layer
and have also contributed to the improvement of channel access performance by
automating the tuning of protocol parameters and the evaluation of networking
protocols. These advancements address critical issues related to the limited
power of wireless devices and privacy concerns [117, 118, 119]. FL has
garnered considerable interest due to its inherent parallelization
capabilities and its potential for greater efficiency compared to centralized
methods in terms of storage and privacy.

The authors in [120] proposed an alternative analog communication approach in
which the devices transmit their local gradient estimates directly over the
wireless channel. The learning process is motivated by an FL strategy where
devices have their local datasets. They communicate with the parameter server
over a wireless MAC. Here, the parameter server is not interested in the
individual gradient vectors but in their average, and the wireless MAC
automatically provides the server with the sum of the gradients.

Non-orthogonal multiple access (NOMA) technology is considered a building
block for 5G and beyond networks. The study discussed in [121] investigates
the organization of machine-type devices (MTD) into clusters for NOMA-based
systems. Focusing on resource allocation, the authors put forth an approach
involving traffic model estimation through FL. In this method, the MTD
autonomously estimates its traffic model parameters and transmits them to the
BS, which subsequently aggregates the traffic model, allocates the suitable
resource blocks, and transmits power to each MTD.

The authors in [122] proposed a NOMA-enhanced wireless network model to offer
NOMA desired channel conditions and increase consumer quality by adopting the
FL concept to enable multiple agents to simultaneously explore similar
environments and exchange experiences.

Ref | Application |  Benefits |  Drawbacks
---|---|---|---
[97, 98, 99] | Symbol / Signal Detection |  Learning collaboratively to clear the received symbols, without the need for channel estimation stage or gathering raw data |  Need deep ML models for better performance
[103, 104] | Channel Estimation |  Ability of each user to estimate its channel with less transmission overhead |  Heavy computation resources due to the labeling phase of the channel data
[110, 111, 113] | Beamforming |  Dynamic beamforming and antenna configurations for optimal signal transmission, improving coverage and reducing interference |  Sub-optimum performance and complex labeling
[115, 103, 116] | RIS |  Achieving high speed convergence in high dimension and complex environment |  Only RIS beamforming is performed
[107, 108] | CSI prediction |  Estimating the accurate CSI when the RF chains or sensors are not equipped on the RIS while preserving users’ privacy |  Need to maximize the training performance and the heterogeneous data should be taken into consideration
[120, 121, 122] | Channel Access |  Tuning automatically individual protocol parameters while improving the convergence speed and communication delay |  The impact of CSI estimation error on the performance needs to be studied.
[123, 124] | Spectrum Sharing |  Improving spectrum sharing efficiency by learning in a distributed way from local information, without requiring the entire data |  Need to collect more real data to analyze the true potential of FL in spectrum sharing
TABLE V: Summary of FL-based contributions at PHY/MAC layers.

###  IV-G Spectrum Sharing

Spectrum efficiency is one of the key performance metrics in 5G communication
networks and even beyond [125]. Advanced spectrum-sharing techniques, such as
ML and DL [126], are generally used to deal with typical challenges while
enhancing efficiency. However, deploying ML techniques for spectrum
applications faces the challenges of limited dataset resources, as well as
privacy concerns. Thus, FL scheme handles privacy challenges while achieving
interesting results compared to centralized learning.

In [123], the authors proposed an ML-based non-coherent spectrum sensing
system named FaIR, which leverages a communication-efficient distributed
learning framework, FL, for environmental sensors to collaborate and train a
data-driven ML model for incumbent detection under minimal communication
bandwidth. The preliminary results show that state-of-the-art spectrum
classification algorithms in an FL environment perform better than traditional
techniques.

In [124], the authors investigated the spectrum sharing problem in high-
mobility vehicular networks by adopting the FTL method. They start by modeling
resource sharing as a multi-agent reinforcement learning problem, which is
then solved using a fingerprint-based deep Q-network method that is amenable
to a distributed implementation. The results show that with a well-designed
reward system and training process, the vehicle-to-vehicle transmitters learn
from their interactions with the communication environment. The authors
developed an effective strategy to collaborate in a distributed manner,
thereby optimizing system-level performance using local information.

###  IV-H Summary

In this section, we have investigated the existing FL-based schemes that
improve communication quality and key performance indicators (KPIs) for
physical and MAC layers in wireless communication systems. The following are
the lessons learned from this section :

  * •

The integration of FL at the PHY and the MAC layers leverages the unique
capabilities of FL to optimize wireless communication processes, enhancing
efficiency, reliability, and performance.

  * •

FL represents a novel approach that redefines how wireless networks operate at
the most fundamental level such as signal detection, channel estimation,
beamforming, RIS optimization, channel access, and spectrum sharing while
presenting certain drawbacks such as model/data/hardware complexity,
communication efficiency, and learning accuracy.

Table V illustrates a summary of the main contributions and their respective
benefits and drawbacks.

##  V Federated Learning at NET Layer

The use of FL in the network layer offers a privacy-preserving, efficient, and
scalable approach to ML. It addresses the challenges posed by distributed and
sensitive data, while also catering to the low-latency and fault-tolerant
requirements of networked systems. It is applied in several tasks, such as
resource management, user behavior prediction, network slicing, routing
optimization, and cloud computing, among others, as detailed next.

###  V-A Traffic Classification

Traffic classification (TC) has received increasing attention in recent years.
It aims to offer the ability to automatically recognize the application that
has generated a given stream of packets from the direct and passive
observation of the individual packets flowing in the network [127]. Due to the
achievement of FL in data privacy protection, it is applied even in internet
traffic classification to improve the accuracy of packet transmission while
preserving private data.

The work [128] proposed an FL-based traffic classification framework named
FLIC that classifies new applications on the fly when a client joins the
learning process with a new application. The designed protocol achieves an
accuracy comparable to the centralized scheme for identifying internet
applications with a privacy guarantee.

The work in [129] built a cross-silo horizontal federated model for TC using
flow-based time-related features. The proposed FL framework performs
comparably to a centralized DL model for internet application identification
without privacy leakage.

In [130], a novel approach using federated semi-supervised learning for
network traffic classification was designed. The federated servers and several
clients work together to train a global classification model, where unlabeled
data is used on the client, and labeled data is used on the server. Results
show that this method protects users’ privacy without sharing a large amount
of labeled data as well as achieving good accuracy.

###  V-B Resource Management

Power and resource allocation remain critical wireless challenges due to the
need for more resources [131]. Optimizing a multi-cell network spectral
efficiency and connectivity invariably results in non-convex resource
allocation issues, which are frequently handled using basic methods such as
successive convex approximation and matching theory. To address the high
complexity and impracticality of traditional approaches, the authors in [132]
and [133] used ML algorithms to solve resource optimization of problems
related to mobile edge computing (MEC). However, sending all local information
to a central controller in MEC-enabled high-altitude balloon (HAB) networks is
impracticable since the transmission of local datasets results in substantial
energy usage. To this end, FL allows dispersed devices to cooperatively train
an ML model by sharing trained parameters with other devices instead of
sending a huge dataset.

The authors in [134] solved this problem using a support vector machine (SVM)
based FL to determine the user association proactively. The proposed method
enables each HAB to cooperatively build an SVM model to determine all user
associations without transmitting computational tasks or user historical
associations to other HABs. The SVM model analyzes the relationship between
the future user association and the data size of the task that each user needs
to process at the current time slot to determine the user association
proactively. Given the prediction of the optimal user association, each user’s
service sequence and task allocation are optimized to minimize the weighted
sum of the energy and time consumption. This approach reduces the weighted sum
of the energy and time consumption of all users by up to 16.1% compared to a
conventional centralized method.

In [135], the authors investigated distributed power allocation for edge users
in decentralized wireless networks. The proposed model (FL-CA) aims to
minimize power consumption while satisfying the user quality of service (QoS)
requirement and protecting user privacy. In the FL framework, edge devices
locally make decisions on power allocation by training a local actor-critic
model and then send the gradients and weight ages generated by the actor-
network to the BS for information aggregation at regular intervals. Moreover,
the authors adopt the federated augmentation algorithm which uses the
Wasserstein generative adversarial network (WGAN) for data augmentation to
overcome the over-fitting problem caused by data leakages. The algorithm
empowers each device to replenish the data buffer using a generative model of
WGANs until reaching an i.i.d training dataset, which reduces the
communication overhead compared to direct data sample exchanges.

In [136], the authors proposed a federated reinforcement learning (FRL) based
channel resource allocation framework for ultra-dense 5G and B5G wireless
networks and suggested collaborative learning estimates for faster learning
convergence. The results demonstrate that the designed FRL model is superior
to non-federated reinforcement learning.

###  V-C Radio Access Network

The radio access network (RAN) is a fundamental part of a mobile
telecommunication system where user data is sensitive and subject to strict
privacy regulations. Centralized ML approaches raise privacy concerns due to
data aggregation. FL allows local model training on devices without sharing
raw data, preserving user privacy while enabling collective model updates
[137, 138, 139].

Deep reinforcement learning was discussed in [140] and associated challenges
in a multi-mobile virtual network operators (MVNOs) environment. The authors
designed a federated deep reinforcement learning mechanism on an O-RAN
architecture to improve the radio resource allocation operation of MVNOs and
illustrate through extensive simulations that the proposed RAN slicing
mechanism enables a better allocation of the needed radio resources to satisfy
users’ QoS requirements in terms of delay and data rate.

The work in [141] proposed a novel FL-based mobility and demand-aware
proactive content offloading (MDPCO) framework that exploits distributed
learning strategies and capitalizes on users’ mobility and demand information
for proactive content offloading. The efficacy of MDCPO improves performance
against local and cloud-based models.

The authors in [142] designed a federated deep reinforcement learning
algorithm to coordinate multiple independent xAPPs in O-RAN for network
slicing by developing two xAPPs, namely a power control xAPP and a slice-based
resource allocation xAPP to enhance learning efficiency and improve network
performance.

###  V-D User Mobility/Location Prediction

Due to the varied quality-of-service requirements, user behavior and wireless
network performance have become crucial for developing and evaluating new
applications and service opportunities. FL strategy is promising in several
situations to predict users’ behavior and maximize the quality of experience
(QoE). Based on the mobility predictions, we provide more information about
the network, the users dynamically choose a subchannel to upload data in the
uplink, the BS dynamically allocates multiple subchannels to multiple users in
the downlink, and multiple users who occupy the same subchannel perform NOMA
or full-duplex. Due to the privacy-sensitive nature of user activities,
existing location prediction approaches [143, 144, 145] rely on centralized
storage of user mobility data for model training, which raises privacy
concerns and risks. FL has the potential to enable predictive features on
smartphones without diminishing the QoE or leaking private information.

The authors in [146] presented an adaptive FL model as a decentralized method
for mobility prediction. They combined a personalized FL model with an
attention network to predict user location in a protected way. This approach
leverages the useful information in the behaviors of massive users to train
accurate mobility prediction models and, meanwhile, remove the need for
centralized storage.

Another interesting work on human trajectory prediction using FL is presented
in [147]. The authors proposed a privacy-preserving human mobility prediction
framework to achieve promising prediction performance while preserving
personal data on local devices. Based on the DL mobility model, no private
data is uploaded to the centralized server. The only uploaded thing is the
updated model parameters which are difficult to crack and thus more secure.

The authors in [148] demonstrated that FL for human activity recognition tasks
produces models with slightly worse, but acceptable accuracy than centralized
techniques. Recently, it has been used in several works and in proactive HO
mm-wave vehicular networks to protect user position information, reduce
communication overhead, and reduce frequency. The authors of [149] proposed a
hierarchical personalized FL (HPFL) approach as a novel client-server
architecture framework to serve FL in user modeling with inconsistent clients.
Traditional FL indiscriminately aggregates and updates the consistent whole
user models, but HPFL attempts to divide and process the different components
of the heterogeneous models independently.

Location information is a service enabler for communication network design,
operations, and optimization [150]. FedLoc is a new collaborative positioning
and location data processing framework proposed in [151]. This approach
properly resolves the question of privacy in target localization and location
data processing in collaboration with many mobile users. The federated
approach is promising thanks to its significant advantages:

  * •

Efficiency in handling the data privacy issue, allowing mobile users to
exchange location-related information safely.

  * •

Collaboration among mobile users facilitates the calibration effort.

  * •

Cell phones are becoming capable platforms for performing complex
calculations.

The work in [152] presented another FL-based approach to improve the accuracy
of received signal strength fingerprint-based localization while protecting
the privacy of the crowdsourcing participants. The main idea of this novel
method is to keep local data where it is generated and convey only local
models throughout the learning process. When employed as a booster for
centralized learning, the suggested approach enhanced localization accuracy by
1.8 meters and reached satisfactory localization accuracy when used alone.

The authors of [153] used FL for human activity recognition (HAR) allowing
each user to handle his activity recognition task safely and collectively.
They designed a perceptive extraction network as the feature extractor for
each user to capture enough features from HAR data. PEN comprised of a
featured network to discover local features and a relation network, a
combination of long short-term memory (LSTM) and attention mechanism, which is
in charge of mining global relationships hidden in the data.

###  V-E Network Slicing

Network slicing enables the shift from a network as an infrastructure
configuration to a network as a service to support a wide range of 5G and 6G
smart services with several requirements [154] [155]. By integrating FL into
network slicing, telecommunication providers and service operators unlock the
potential of personalized services while maintaining data privacy and network
performance. As the demand for customizable and secure services grows [156],
the combination of FL and network slicing is likely to play a significant role
in the future of telecommunications [157].

The authors in [158] proposed integrating an FL framework with mobile edge
systems to train double-deep Q-learning agents at the network edge for caching
and computational offloading decisions while protecting user privacy. The “In-
Edge AI” was assessed and shown the capacity to achieve near-optimal
performance with a reasonably minimal learning overhead, while the system is
cognitive and adaptive to mobile communication networks.

In [159], the authors developed an FL-based approach to enhance resource
allocation strategy in multi-industrial IoT. This novel proposal deep
federated Q-learning, involves IoT slices’ resource allocation in terms of
transmission power and spreading factor according to the slices QoS’
requirements.

The authors of [160] adopted FL to predict slices’ service-oriented KPIs by
keeping raw data where it is generated and sending only users’ local models to
the centralized entity for aggregation. The obtained results showed the
efficiency of the designed approach in reaching good prediction accuracy while
ensuring privacy issues.

Aiming to improve network throughput while reducing hand-off cost, the authors
of [161] designed an efficient device association scheme for RAN slicing by
exploiting a hybrid FRL framework. Two levels of aggregation were proposed for
the device association problem. One involves the same type of services to
aggregate the local parameter models to share similar samples. The other one
pertains to the different types of services to aggregate access features to
make an optimal global decision on network slicing and BS selection.

The authors of [162] investigated the distributed network slicing for 5G. They
introduced a novel framework centered around a federated orchestrator in the
control plane. This entity is responsible for coordinating spectrum and
computational resources, all without requiring any exchange of personal data
or resource information from the local BS. This framework significantly
reduces service response time for both supported services, especially compared
to network slicing with only a single resource.

###  V-F Routing Optimization

Predictive routing employs the power of AI to constantly evaluate real-time
data to anticipate outcomes [163]. Most existing ML techniques to drive
routing and traffic control choices are essential [164]. However, they rarely
focus on challenging networks, characterized by constantly changing network
conditions and a significant volume of traffic generated by edge devices.
Several FL systems are proposed to address those problems and optimize routing
decisions in challenging scenarios.

To increase performance and facilitate the scalability of data-intensive
applications, the authors in [165] designed a federated architecture for
routing packets inside a distributed edge network. They proposed a novel path
selection model to predict the best route using LSTM. When a peak load is
anticipated, the controller leverages this information to make adjustments to
routing. By employing FL, all controllers attain a global view of the
infrastructure while exchanging relatively little information, which helps
organizations protect the bandwidth for the application traffic.

The authors of [166] developed mobility-aware routing and caching strategies
for dense small-cell networks based on the FL framework to optimize the cache
placement and minimize the network cost. They first started by segmenting the
whole geographical region into small sections, each with one small BS (SBS)
and many mobilized users (MUs). They proposed a federated routing and
popularity learning strategy in which the SBSs jointly learn the routing and
preferences of their respective MUs and make caching decisions.

In [167], the authors created a decentralized routing application on time-
dependent transportation networks using FL to learn shared prediction models
online. All routing occurs at the network edge within a private roadside unit
fog network, where access to outside cloud services is assumed to be
intermittent. This system avoids the costly transfer of raw data between
processes.

Ref |  Application |  Benefits |  Drawbacks
---|---|---|---
[128, 129, 168, 130] |  Traffic Classification |  Safely classifying internet traffic as well improving the accuracy of packet transmission |  More improvements are needed to deal with complex data and information security
[134, 135, 136] |  Resource management |  FL facilitates adaptive resource allocation, distributing bandwidth, power, and other resources intelligently across network nodes |  Need more evaluations by considering for practical applications
[140, 141, 142] |  Radio access network |  Satisfy users’ QoS requirements in terms of delay and data rate |  Need more enhancement privacy beyond what FL inherently promotes
[146, 147, 148, 149, 151, 152, 153] |  User mobility/location prediction |  Enabling predictive features on smartphones without diminishing the QoE or leaking private information |  Mobile terminals should be able to process a modest amount of data and perform analysis with FL frameworks
[158, 159, 160, 161, 162] |  Network slicing |  Offering significant improvement on service latency performance for all supported services |  Updating local models with high frequency increases the network overhead and consumes network and computing resources
[165, 166, 167] |  Routing optimization |  Optimizing routing for challenging scenarios while avoiding costly transfer of raw traffic data and reducing bandwidth stress |  Need more travel time improvement and grid prediction models to mitigate the impact of errors on user trips
[169, 170, 171, 172, 173, 174, 175] |  UAV flying networks |  Handling target challenges in UAV systems as well as operating at a considerably lower communication cost |  Need to accelerate the strategy-making process and the resource allocation should be optimized
[176, 177] |  Underwater Networks |  Enhancing underwater communication systems performance with high transmit power and secure underwater scenarios |  The learning process should be more accelerated and applied in real scenarios
[178, 179, 180, 181, 182, 183] |  Cloud/Fog computing |  Achieving less energy consumption to make full use of the big data in both cloud and fog computing |  The training efficiency has to be enhanced as well as optimizing some critical parameters
TABLE VI: Summary of FL-based contributions at NET layer.

###  V-G Unmanned Aerial Vehicle

The combination of AI and Unmanned Aerial Vehicles (UAVs), commonly known as
drones, holds significant promise for various industries and applications
[184, 185, 186]. However, it presents challenges related to regulation,
safety, privacy, security, and complexity. FL is an emerging and promising
application that combines privacy preservation, low latency, low communication
overhead, and real-time learning with the capabilities of UAVs [187].

The work in [169] presented an FL-based drone authentication model with
drones’ RF features in IoT networks. The deep NN (DNN) architecture is used,
with stochastic gradient descent optimization performed locally on the drones.
Results show that the federated drone authentication model gains a high true
positive rate during drone authentication and better performance compared to
other ML systems.

The study in [170] adopted FL to facilitate privacy-preserving sensing and
collaborative learning in the UAV services. This work proposed a multi-
dimensional contract matching design that aims to match the most optimal UAV
to each sensing sub-region while accounting for UAV heterogeneity.

The authors of [171] proposed a hierarchical nested personalized FL, a
holistic distributed ML framework for personalized model training across the
worker leader core network hierarchy. This method used data sharing across
device clusters to create tailored local models.

In [172], a secured FL framework for UAV-assisted MCS was presented. Firstly,
three attacks were proposed, and the related countermeasures were studied to
secure cooperative learning for UAVs. Secondly, an FL approach has been
developed using the blockchain network to securely store and trace UAV
contributions in an immutable way while protecting local model exchange across
UAVs. Finally, a privacy-preserving local model-sharing mechanism was
developed to protect UAVs with high aggregation accuracy.

The authors of [173] proposed a novel architecture for implementing
distributed learning algorithms inside a UAV swarm that consists of a leading
UAV and several following UAVs. Each UAV trains a local FL model based on its
gathered data and then sends it to the leading UAV, aggregating the received
models, generating a global federated model, and transmitting the knowledge to
followers through the intra-swarm network.

In [174], the authors proposed FL-aided multi-UAV systems for task
classification in the broad application area of exploration scenarios. At
first, local training is carried out by each UAV relying on its own locally
gathered images; the model developed from the local training is then
transmitted to the GFC over fading wireless channels. Finally, a global model
is built at the GFC and sent back to each UAV for the next local model update.
This framework operates at a considerably lower communication cost compared to
traditional ML.

FL is a promising solution for flying ad-hoc networks (FANET). The authors of
[175] designed an FL-based on-device jamming attack detection security
architecture for FANET. They added a client group prioritization technique
leveraging the Dempster–Shafer theory [188], which allows the aggregator node
to identify better client groups for calculating the global update.

###  V-H Underwater Networks

Underwater wireless communication is another type of wireless communication in
which acoustic signals across an underwater channel send digital information.
Numerous efforts have been made to enhance the performance of underwater
communication systems using ML, DL, and transfer learning techniques. However,
the set-up of underwater networks is challenging because of the high
propagation delay and high transmit power compared to wireless free-space air
networks [189]. FL mechanism holds promise as a secure means to collectively
train underwater communication models [190].

The authors of [176] proposed a novel multi-agent deep reinforcement learning
algorithm to remove unexpected smart ocean environment changes and channel
unreliability. First, each device conducts distributed DL training with
personal data, aggregating the output at a centralized station. Then, the
centralized machine computes global optimal solutions and then distributes the
results to the associated participants. This approach effectively enhances the
throughput performance in underwater scenarios.

For the ocean of things, the authors in [177] developed a federated meta-
learning enhanced acoustic radio cooperative framework that takes advantage of
data distributed across surface nodes to train the DL-receiver in the context
of random scheduling wireless networks. This approach enables distributed
transfer learning to be adapted to new datasets.

###  V-I Cloud/Fog Computing

While the cloud proves a powerful computing platform in numerous applications,
it encounters difficulties in scenarios with rigorous requirements including
drones and autonomous vehicles that need low-latency and privacy-preserving
[191]. FL is promising for training ML models on mobile devices while ensuring
limited computing, storage, energy, and bandwidth.

In [178], the authors presented a highly efficient FL framework with strong
privacy preservation in cloud computing. Instead of noise injection methods, a
lightweight encryption protocol is employed, giving both provable privacy and
desirable model utility. This approach provides secure training to learn from
all available data.

Existing FL approaches for cloud-based AI IoT (AIoT) applications need more
accurate prediction results. The authors in [179] presented a collaborative
architecture that deploys DNN models across both cloud and AIoT devices. They
designed two-stage training and co-inference schemes to improve the prediction
accuracy of individual AIoT devices using little branches, reducing their
average inference time with the help of the big branch part deployed on the
cloud.

A client-edge-cloud hierarchical FL system was presented in [180] based on the
HierFAVG algorithm that allows multiple edge servers to perform partial model
aggregation. This framework simultaneously reduces the model training time and
the energy consumption of the end devices compared to traditional cloud-based
FL.

To take full advantage of fog computing and AI [192], a new paradigm of FL-
enabled intelligent fog radio access networks was proposed in [181]. This
framework supports intelligent signal processing and network management with
low communication cost and high efficiency, providing a feasible approach to
implementing network edge intelligence.

The authors in [182] used FL to predict content popularity in fog radio access
networks and reduce computational complexity. The inputs of the prediction
model are the averaged popularity scores for clustered users’ contents, the
model is then learned automatically by training the algorithm with historical
popularity and the pre-processed inputs.

To deal with the privacy gap in fog computing, the authors of [183] created a
blockchain FL system that enables autonomous ML without any centralized
authority to maintain the global model and coordinates using blockchain. The
latency performance derives the optimal block generation rate by considering
communication, consensus delays, and computation costs.

###  V-J Summary

This section covers recent findings on applying FL mechanisms at the network
layer. This emerging paradigm effectively approaches the results obtained by
centralized ML techniques while guaranteeing 5G and 6G requirements such as
user privacy, latency, limited computing, energy, and bandwidth. The lessons
learned from this section are as follows:

  * •

The application of FL at the network layer redefines how networks are managed,
optimized, and evolved. By leveraging FL’s decentralized learning approach,
the network layer gains the ability to enhance its performance,
responsiveness, and intelligence while addressing critical challenges (e.g.,
privacy).

  * •

While FL applications at the network layer promise numerous benefits, they
also present some challenges including communication overhead,
synchronization, security, and scalability.

Table VI illustrates a summary of FL-based contributions that enhance the
network layer architecture.

##  VI Federated Learning at APP/Transport Layers

The application and the transport layers are the last components of the OSI
model that specify the shared protocols and interface methods used by hosts in
a communications network. FL significantly improves handling APP layers
challenges in wireless communication networks. In this section, we provide a
deep dive into recent FL contributions in the context of APP and transport
layers.

###  VI-A Transport Performance

FL is applied to enhance the performance and efficiency of the data transport
protocol and the transmission control protocol (TCP) used for reliable data
transmission over the Internet. It brings several benefits, including improved
congestion control, network management, and protocol optimization [193].

To enhance the performance of the internet of vehicles, the authors in [194]
proposed a privacy-preserving FL framework that guarantees ubiquitous
connection among the vehicles and presented a local mechanism to satisfy the
reliability and latency requirements. This work introduced a mathematical
framework that accurately estimates TCP-perceived packet loss probability and
latency for vehicles when wireless channels suffer from different magnitude
losses and transmission delays.

The authors of [195] adopted FL to study the performance of long-lived
compound TCP (C-TCP) flows over Industry 4.0 WiFi infrastructure. They
developed a novel analytical model to investigate the impacts of collisions,
wireless losses, and AP buffer losses on C-TCP over WiFi with bidirectional
flows. The results demonstrate that cognitive radio and FL significantly
enhance the performance of the industrial multiple scenarios.

###  VI-B Cooperative and Distributed Computing

Distributed computing is a paradigm where elements of a software system are
distributed across multiple computers, yet function collectively as a unified
system. This strategy is employed to enhance efficiency and performance.
Combining AI and distributed computing poses challenges in data management and
communication between nodes. FL paradigm is used to safely manage distributed
computing systems by training a shared model collaboratively while keeping all
data on their devices. In [196], the authors designed a distributed computing
defense framework for a sustainable society by combining FL and blockchain to
preserve privacy and unlock the full potential of ML in distributed computing
environments. Compared to traditional approaches, the result outcomes are
promising in terms of accuracy and loss.

Recently, cooperative computing has emerged as a novel paradigm within the
domain of distributed strategies. This approach revolves around fostering
collaboration and synergy among various computing nodes to achieve enhanced
system performance and efficiency. It capitalizes on the collective
intelligence and resources of individual components, paving the way for
innovative solutions that address complex challenges. In [197], the authors
introduced a novel FL framework that leverages the cooperation of devices that
perform data operations inside the network by iterating local computations and
mutual interactions via consensus-based methods. This method paves the way for
FL integration in 5G and beyond, characterized by decentralized connectivity
and computing.

###  VI-C Crowdsourcing / Crowdsensing

Crowdsourcing has the potential to be highly advantageous to accomplish
computing tasks due to its ability to rapidly collect information [198]. FL is
used to safely implement crowd computing. It involves training ML models using
decentralized data from multiple servers, without transferring the data to a
central location. This helps to protect sensitive data while sharing learning
and analysis.

The authors of [199] presented a crowdsourcing framework based on FL,
blockchain, and re-encryption technology. All blockchain nodes participate in
crowdsourcing to retain a full data backup. By adopting FL, each worker
uploads the model or gradient data to clients for aggregation. This approach
allows users to implement decentralized crowdsourcing with less overhead and
higher security while improving the computing audit ability.

The work in [200] studied FL and local differential privacy to facilitate the
crowdsourcing use cases. This approach enables vehicular crowdsourcing
applications to train ML models and predict traffic status while preserving
privacy and reducing communication costs.

Recently, crowdsensing has gained significant attention and becoming an
appealing paradigm for sensor-based data collection [201]. However, the
diverse and sensitive nature of data collected through crowdsensing
necessitates robust privacy protection mechanisms to secure individual user
identities and personal information. Addressing this challenge is imperative
to unlock the full potential of crowdsensing while upholding privacy
standards. Several recent contributions employed FL paradigms to overcome
those limitations. In [202], a federated crowdsensing framework was proposed
to analyze the privacy concerns of the four crowdsensing stages: creation
task, assignment task, execution task, and data aggregation task.

The authors of [203] integrated FL into mobile crowdsourcing to design a
privacy-preserving system named PriFedAvg where the participants locally
process sensing data via FL and protect training by only uploading the
encrypted training models.

The quality-aware user recruitment problem was investigated in [204], using FL
to predict the quality of sensed data from different users by analyzing the
correlation between data and context information through FL. This method
improves the quality of sensed compared with traditional algorithms.

A novel approach was developed in [205] for fake task detection based on
horizontal FL. Two ML algorithms and datasets were implemented to identify
fake tasks containing several independent detection devices and an aggregation
entity.

###  VI-D Quality of Experience

The evolution of streaming services needs high data rates, low latency, and
good QoS. Several challenges must be addressed while delivering a specific
service including the large number of devices, the heterogeneous networks, and
the uncontrollable environments. The quality of experience (QoE) concept is
gaining tremendous research efforts to improve and deliver reliable and added-
value services, at a high user experience [206]. ML-based QoE models suffer
from overfitting due to low data volume and limited participant profiles
[207]. A privacy-preserving ML scheme such as FL, enables the sharing of QoE
data models between all participants only by transmitting model parameters.

The authors in [208] adopted FL and round-robin learning, where the model is
trained sequentially amongst the collaborated partner nodes to show that high
accuracy is achieved without sharing sensitive data between participants.

To facilitate smooth QoE model management and obtain higher accuracy, the work
in [209] presented a set-based Bayesian synthetic data generation method for
FL that effectively reduces the training time by 30% and the network footprint
in the communication channel by 60%.

###  VI-E Cybersecurity

Cybersecurity benefits from AI techniques to enhance security performance and
better protection against complex threats [210]. However, traditional ML
architecture does not meet the demand of cybersecurity because of the large
amount of data, the data heterogeneity, the high data velocity, and the major
existing challenges in gathering cyber intelligence and attack datasets from
distributed resources. The FL paradigm is used to securely train models for
anomaly detection tasks without collecting raw data [211].

A privacy-preserving approach was proposed in [212] to support verification of
the server’s calculation results to each user which allows all participants to
verify the results’ correctness returned from the central server with
acceptable overhead.

The authors of [213] designed an FL-based framework for malicious URL
detection in a managed security service provider setting. They looked at
various scenarios regarding data partitioning amongst agents and found that
the collaborative federated ML model enhanced URL classification performance
in all scenarios and improved its rates up to 27%.

In [214], the authors presented the FedDICE framework that integrates FL to
SDN-oriented security architecture to enable collaborative learning,
detection, and mitigation of ransomware attacks in collaborative environments.
This approach achieves performance similar to centralized learning results.

The work in [215] developed an intelligent threat defense system employing
blockchain FL, which seeks to fully upgrade the way passive intelligent
systems operate, aiming at intelligently classifying smart cities networks
traffic derived from industrial IoT by deep content inspection methods to
identify anomalies that are usually due to advanced persistent threat attacks.

###  VI-F Summary

In this section, we present a comprehensive literature review on applying FL
at the application and transport layers. There are many improvements and
promising solutions that have an impact on the performance of transport,
distributed computing, crowdsourcing, cybersecurity, and QoE. The lessons
learned from this section are :

  * •

Applying FL at both the transport and the application layers introduces a
synergistic approach that enhances how data is transported, processed, and
utilized across the network. This integration offers the potential to optimize
both the technical aspects of data transmission and the functional aspects of
user-facing applications.

  * •

Ensuring secure data transmission, managing synchronization, optimizing
communication overhead, and addressing scalability are considerations that
require ongoing attention and innovation.

Table VII provides a review of the FL contributions at the APP and the
transport layers, highlighting their benefits and drawbacks.

Ref |  Application |  Benefits |  Drawbacks
---|---|---|---
[194, 195] |  Transport performance |  Enhancing TCP performance while preventing privacy leakage |  Must implemented in complex networks to analyze the convergence and throughput fairness
[196, 197] |  Cooperative and Distributed Computing |  Securely manage distributed and cooperative systems without requiring large computation |  Requiring more efficient use of the limited bandwidth, including quantization, compression, or ad hoc channel encoding
[199, 200, 202, 203, 204, 205] |  Crowdsourcing and Crowdsensing |  Facilitating crowdsourcing and crowdsensing applications with less overhead, less communication cost and higher security |  Need to study the waiting for charging as most crowdsensing tasks have temporal deadlines
[208, 209] |  QoE |  Enabling a seamless QoE model management without having to use sensitive data |  Insufficient performance due to the difficulties in accessing QoE datasets
[212, 213, 214, 215] |  Cybersecurity |  Effectively detecting attacks while addressing issues of data sharing |  Have to apply the FL strategy when the attackers influence the model training
TABLE VII: Summary of FL-based contributions at Transport/APP layers.

##  VII Federated Learning Applications & Verticals

In this section, we discuss the benefits of adopting an FL scheme in wireless
network scenarios, including autonomous driving, anomaly detection, industrial
operations, smart healthcare, and other applications.

###  VII-A Autonomous Driving

Autonomous driving and autonomous vehicles are now among the most prominent
research [216]. They involve the application of ML and DL techniques that
actively select data, transform information, control processes, and make
decisions [217]. However, the sequential process of centralized architectures
is extremely time-consuming, and the knowledge from the fine-tuned model stays
local and not leveraged. Thus, FL is considered to be a strong mechanism that
enables devices to interact with their environment and acquire knowledge
safely from other devices [218].

To design the autonomous controller of connected and autonomous vehicles (CAV)
under the conditions of wireless link uncertainty and environmental dynamics.
The work in [219] presented a new DL framework, a dynamic federated proximal
algorithm for the training process that introduces a regularizer at the CAVs
to minimize the impact of non-IID and unbalanced data on the FL convergence.

In [220], the authors integrated FL into autonomous driving to preserve
vehicular privacy and improve model accuracy by keeping original data in a
local vehicle and only sharing the training model parameter through the MEC
server. The results show that the proposed FL-based autonomous driving system
reduces 73.7 % of training loss, and improves the accuracy to around 5.55 %.

For privacy-aware and efficient vehicular communication networking, the
authors in [221] built an autonomous blockchain-based FL system to ensure end-
to-end trustworthiness and delay. The proposed framework enables effective
autonomous vehicle communication by allowing local on-vehicle ML modules to
share and validate their updates in a fully decentralized manner.

The work in [222] designed an online federated reinforcement transfer learning
process to extract knowledge for autonomous vehicles in real time. In this
model, all the participant agents make corresponding actions with the
knowledge learned by others, even when they are acting in very different
environments. The results show that the proposed approach effectively
transfers knowledge online, with better training speed and performance.

To minimize the network-wide power consumption of vehicular users while
ensuring high reliability in terms of probabilistic queuing delays, the work
presented in [223] proposed a novel joint transmit power and resource
allocation using FL, which reduces unnecessary overheads.

The authors of [224] proposed a new type of FL vehicular network concept
namely FVN, which offers more consistent performance than a traditional
vehicle network and handles data/computation-intensive applications. The
obtained results demonstrate that the proposed framework provides high
accuracy and less energy consumption.

###  VII-B Anomaly Detection

FL potentially admits meaningful applications for malicious attack detection
in mobile communication systems. For instance, DIoT is an autonomous self-
learning distributed system for detecting compromised IoT devices. The
approach designed in [225], FL efficiently aggregates behavior profiles while
relying on device-type-specific communication profiles without human
intervention or labeled data.

In [226], the authors adopted FL for network anomaly detection and analyzing
network traffic to tackle the data scarcity problem and preserve data privacy.
It’s about designing a multi-task DNN to perform network anomaly detection
task, traffic recognition task, and traffic classification task.

An anomaly detection architecture for industrial IoT-based smart manufacturing
was presented in [227]. This architecture detects anomalies for time series
data typically running inside an industrial system by only sending the
training model of each edge to the cloud for the global update. This approach
saves 35% of the bandwidth consumed in the transmission link between the edge
and the cloud.

In [228], the authors developed an intelligent intrusion detection framework
using FL. First, the LSTM global model is deployed on the servers. Second,
each user trains its local model and then uploads its parameters to the
central server. Finally, the central server performs the model parameters
aggregation to distribute the new knowledge to all participants. This approach
achieves higher accuracy and better consistency than conventional methods.

###  VII-C Industrial Operations

Industrial operations refer to the development of many domains, including
image processing, robotics, manufacturing, agriculture, and other fields. ML
and DL techniques were applied to solve various industrial problems [229].
However, traditional centralized learning does not meet the requirements of
industrial scenarios because of privacy and scalability concerns. Several
works were designed and implemented using FL to address these challenges while
generating potential results [230].

The authors in [231] developed an efficient and privacy-enhanced FL (PEFL)
framework to solve various industrial challenging problems in industrial AI.
PEFL scheme is non-interactive which prevents privacy leakage from the local
gradients and the shared parameters even when an adversary colludes with
multiple entities. Results demonstrate the advantage of this approach in terms
of accuracy and efficiency.

A verifiable FL (VFL) mechanism with privacy-preserving for big data in
industrial IoT was proposed in [232]. This framework effectively ensures the
model’s security and private gradient by allowing each participant to verify
the aggregated results based on Lagrange interpolation, and the verification
overhead remains constant regardless of the number of participants. The VFL is
applied to many industrial scenarios because of its advantages in terms of
verification and total overhead.

To provide a structured collection of requirements and workflows covered in an
industrial FL (IFL) architecture, the authors of [233] designed an IFL system
supporting knowledge exchange in continuously evaluated and updated FL cohorts
of learning tasks with sufficient data similarity. The proposed scheme enables
optimal collaboration of business partners in common ML problems, prevents
negative knowledge transfer, and ensures resource optimization of involved
edge devices.

The authors of [234] proposed a new architecture of digital twin (DT)
empowered industrial IoT in which DTs sensitively capture the dynamic changes
of the network. The proposed scheme adjusts the aggregation frequency
according to the channel state and provides better performance in terms of
learning accuracy, convergence rate, and energy saving.

FL offers efficient solutions to collaborative learning in decentralized
multi-robot and distributed autonomous systems [235]. The authors of [236]
proposed a real-time data processing architecture for multi-robots based on
differential FL where a global model is trained on the cloud iteratively and
distributed to multiple edge robots. This architecture is applied to multiple
robotic recognition tasks and balances the trade-off between performance and
privacy.

###  VII-D Ultra Reliable Low Latency Communications

Ultra-reliable low latency communications (URLLC) is one of the key pillars of
5G NR [237] for extremely low latency (e.g., 1ms) and high reliability (e.g.,
99.999%). With recent advances in data-driven ML and DL, it is possible to
learn a wide range of policies for wireless networks. However, those methods
need:

  * •

Long training phase.

  * •

Large number of training samples.

  * •

Long time to obtain enough training samples.

  * •

Data access permission.

FL is a promising procedure that safely and collaboratively trains ML models
and improves URLLC requirements, especially federated reinforcement learning
which is one of the distributed setups that learns the environment across
numerous decentralized devices without sharing their private data.

A deep reinforcement model was employed in [238] to develop a multi-level
architecture that enables device intelligence, edge intelligence, and cloud
intelligence at the user level, cell level, and network level, respectively
for URLLC. Considering the computing capacity of each user and each mobile
edge computing server is limited, the authors applied FL to improve the
learning efficiency.

The work in [239] presented a novel distributed, FL-based, joint transmit
power and resource allocation framework to improve URLLC in vehicular
communication. The extreme value theory was adopted to characterize the
constraints in terms of URLLC by estimating the tail distribution locally
without sharing the actual queue length samples. This approach reduces the
amount of exchanged data and unnecessary overheads.

Addressing URLLC’s scalability and compatibility with other URLLC/non-URLLC
services is a big problem. The authors in [240] proposed a hybrid multiple
access solution for addressing such issues in a spectrum-/energy-efficient
way, by leveraging a distributed hierarchical ML approach for proactive radio
resource management to different URLLC traffic streams. Results show the
potential of the FL-based scheme that increases 75% data rate and 99.99%
reliability for both scheduled and non-scheduled traffic types.

###  VII-E Tactile Internet

The tactile internet (TI) is the next step of the internet of things,
comprising human-to-machine and machine-to-machine communication. It mainly
focuses on providing real-time interactive techniques with a portfolio of
engineering, social, and commercial use cases that require a high degree of
reliability and latency [241]. AI techniques will play an important role in
addressing TI challenges [242], especially FL which has a huge potential to
optimize energy, resources, and transmission delay for tactile internet
applications [243].

The authors of [244] used FRL to meet the requirements of B5G mobile
communication, especially at the MAC and PHY layers channel access mechanisms
that enable URLLC in 5G NR for tactile internet. The FRL is an emerging
solution to handle emerging wireless network scenarios since it learns from
unlabeled data and avoids the expectation of a formulated Markov decision
process to make optimal decisions, routing, and link selection.

###  VII-F Virtual/Augmented/Extended Reality & Metaverse

Virtual reality (VR) is a computer-generated environment with realistic-
looking scenes and objects that give the user the feeling of being completely
immersed in their surroundings. Deploying this new technology over wireless
networks is an essential stepping stone towards flexible deployment of
pervasive VR applications [245]. Minimizing the occurrence of the break-in
presence (BIP) is one of the key challenges in VR that detaches the users from
their virtual world. The authors of [246] designed a novel model that jointly
considers the VR application type, transmission delay, VR video quality, and
users’ awareness of the virtual environment to measure the BIP for wireless VR
users. They adopted an FL strategy to enable multiple BSs to locally train
their model using the gathered data and cooperatively build a global model to
predict the entire users’ locations and orientations. Compared to centralized
approaches, this method achieves significant performance by reducing the
users’ BIP.

Augmented reality (AR) is an enhanced version of the real physical world that
is achieved using digital visual elements, sound, or other sensory stimuli
delivered via technology. Driven by computer vision and AI tools, AR
technology has shown a strong momentum of development [247]. However, the
bandwidth available to transmit and process large amounts of generated data is
extremely limited, making it extremely difficult for AR to detect and classify
objects and achieve their goals of perfectly combining the corresponding
virtual contents with the real world. To solve the classification challenge
for latency-sensitive AR applications that require a high data rate, the work
in [248] proposed a framework that combines FL with mobile edge computing
(MEC) to obtain the global optimal ML model. Evaluations show that the
presented approach requires significantly fewer training iterations compared
to traditional centralized frameworks. To solve the privacy issues in AR, the
authors of [249] proposed a multi-user AR output strategy model using a
hierarchical federated reinforcement learning method for generating and
aggregating the AR output strategy model for multiple users. This framework
effectively learns the information of multi-user AR scenarios and improves the
robustness of the adaptive output strategy model.

Extended reality (XR) refers to all real and virtual environments and human-
machine interactions generated by computer technology and wearable devices
[250]. XR technologies collect and process huge amounts of detailed and
personal data about users, FL is a promising method to handle such challenges
while reducing costs and preserving dispersed datasets. The authors in [251]
investigated distributed FL methods in V2X networks for augmenting the
capability of road user/object classification based on Lidar data, where the
model sharing is implemented on a large number of layers, depending on the
required efficiency and bandwidth requirements.

The metaverse refers to a virtual shared space, often encompassing AR, VR, XR,
and other immersive digital environments. It’s an interconnected digital
universe where users interact, socialize, work, and engage in a wide range of
activities [252]. Applying FL to the metaverse protects the data privacy of
clients and reduces the need for high computing power and high memory on
servers [253].

###  VII-G Smart Healthcare

ML and DL techniques have shown potential for accelerating the advancement of
smart healthcare. However, in several healthcare situations, the limited
amount of data available does not allow for building very powerful models to
investigate the existing challenges. To this end, FL is employed to allow
medical institutions to share their experiences, and not their data, with a
guarantee of privacy to safely perform personalized healthcare and biomedical
tasks [254, 255].

The authors of [256] developed the FedHealth framework using FTL that
aggregates the data from the distributed organizations to train powerful ML
algorithms while preserving client privacy. Following the initial training of
a global model in the cloud, FedHealth leverages transfer learning to create
personalized models for each organization. These personalized models are
continually updated by transmitting the modified global model parameters back
to the respective organizations.

In [257], the authors proposed an edge computing-empowered FL-based system
model to facilitate privacy-preserving collaborative model training across
distributed participants for the development of healthcare applications. The
result of the present approach shows that the contract design is self-
revealing across two time periods and returns higher profits for the model
owner as compared to the uniform strategy.

The clustered FL was adopted in [258] to develop an automatic diagnosis system
for the COVID-19 pandemic. This approach allows remote healthcare institutions
to benefit from collaborative learning without sharing local data and
associated information.

###  VII-H Recommendation Systems

Recommendation systems in wireless networks play a significant role in
enhancing user experiences, optimizing network resource allocation, and
improving overall network efficiency [259]. Implementing efficient
recommendation systems in wireless networks requires a combination of data
analytics, ML algorithms, and real-time data processing. This encompasses the
collection and analysis of user behavior, network performance metrics, and
contextual data [260]. FL has recently been applied to recommendation systems
to protect user privacy and boost prediction accuracy [261]. In FL settings,
recommendation systems train recommendation models by collecting the
intermediate parameters instead of the real user data, which greatly enhances
user privacy [262]. A privacy-preserved recommender system framework was
proposed in [263] by integrating FL architecture to enable the recommendation
algorithm to be trained and carry out inference without centrally collecting
users’ private data. This approach reduces the privacy leakage risk, satisfies
legal and regulatory requirements, and allows various recommendation
algorithms to be applied.

Ref |  Application |  Benefits |  Drawbacks
---|---|---|---
[219, 220, 221, 222, 223, 224] |  Autonomous driving |  Achieving potential learning across several decentralized vehicles by only sharing local model parameters |  Need to limit the increase in the overall delay and avoid the privacy leakage risk
[225, 226, 227, 228] |  Anomaly detection |  Improving the scalability of malicious attacks detection for distributed wireless communication networks |  The training phase should be optimized in terms of weights communicating and performance
[238, 239, 240] |  URLLC |  Decreasing service delay and providing high reliability while preserving user’s information |  Cannot handle the non-IID training data and a large number of connected devices
[244] |  Tactile internet |  Achieving intelligent and optimized network control and resource allocation for URLLC requirements |  Each device must learn the optimal decision with the help of exploration
[246, 248, 249, 251] |  VR/AR/XR and Metaverse |  Improving the generation of virtual objects by optimizing the resource management and reducing the overall latency for VR/AR users |  The security problems caused by non-visual output along with the conflict between multiple output types
[256, 257, 258] |  Smart healthcare |  Allowing organizations to benefit securely from distributed medical experiences |  Must explore the performance in varied distributed data to enhance efficiency and flexibility
[260, 261, 262, 263] |  Recommendation system |  Training a distributed recommender system while protecting user privacy |  The communication between the user and the server leads to increased overhead and latency
TABLE VIII: Summary of FL-based verticals and applications.

###  VII-I Summary

In this section, we have presented a comprehensive summary of the latest
research related to FL in 6G systems. We have focused on highlighting the
pivotal enhancements gained through the adoption of FL in contrast to
centralized ML. A summarized overview is found in Table VIII.

##  VIII Insights and Open Problems

FL has gained tremendous interest in wireless communication networks,
providing better and safer methods that effectively enhance mobile operations.
Lately, numerous substantial FL-based advances in wireless systems have been
developed promising a wide deployment in future 6G. However, FL is still a
nascent research with several challenging issues such as to privacy and
security risks, data scarcity/quality, system heterogeneity, complexity, and
convergence [264]. In this section, we discuss open problems of FL and provide
a summary of recent solutions, insights, and opportunities.

Figure 6: Year-wise publications of FL for mobile networks [generated from all
cited papers]. Figure 7: Proportion of FL-based approaches per network layer
[generated from all cited papers].

###  VIII-A Lessons Learned

In this tutorial, we compiled, classified, and carefully analyzed recent
results and pioneering research breakthroughs related to FL. As illustrated in
Fig. 6, the number of FL learning contributions in mobile networks has grown
steadily over the past seven years, and widely applied to network design, as
visualized in Fig. 7. Indeed, FL leverages efficient and effective solutions
compared to traditional AI techniques to handle wireless problems while
preserving privacy. To sum up, the lessons learned from our study are:

  * •

6G and beyond call for high network flexibility, reliability, privacy, and
intelligence. Those desired features are efficiently and effectively provided
by FL approaches.

  * •

FL has the potential to shape next-generation wireless systems by enhancing
the protocol stack and meeting future demands of data-intensive applications.
By incorporating FL across multiple network layers, wireless systems will
benefit from fundamental values such as sustainability, trustworthiness,
fairness, reduced communication overhead, high resilience and neverseen degree
of versatility.

  * •

FL support by wireless networks has the potential to revolutionize several
industries, from healthcare and manufacturing to smart cities and smart
farming, as it enables novel personalized services, efficient resource
management, and data-driven decision-making.

  * •

FL is speculated to be a transformative approach for the evolution of future
wireless communication systems. However, challenges like communication
efficiency, model aggregation, and security still need to be addressed to
fully harness FL potential in 6G and future wireless networks. Integrating FL
into the design loop of future networks will redefine their capabilities in
terms of performance, privacy, resilience, and intelligence.

###  VIII-B Federated Learning Challenges

Through our systematic review of existing literature, we have identified some
primary bottlenecks that dominate current FL research:

####  VIII-B1 Privacy and Security

Contrary to decentralized schemes, federated learning was designed to provide
a secure machine learning among distributed learning agents. However, data
privacy is still challenging due to the presence of attackers such as on-
device malicious clients to malicious users with only a black-box access to
the model [265]. Although local data might not escape the device, an intruder
still can learn the presence of a data point used for training [52]. Many
efforts have been spent to deal with this issue using transfer ML, secure
aggregation, and attack recognition [266, 267].

####  VIII-B2 Complexity and Convergence

Federated learning suffers from high computational complexity during training
due to running consensus algorithms over heterogeneous massive environments
over perturbed/faulty communication systems. It includes a large number of
handsets with data generated ruled by diverse distributions, which results in
high communication overhead. The goal of FL is to train high-quality models
with lower communication complexity and faster convergence, hence it is
crucial to design efficient schemes to overcome such a limitation. In [268],
two solutions were designed to reduce uplink communication costs: learning an
update directly from a reduced set of variables, and learning a complete model
update and compressing it before sending it to the server. Based on gradient
descent, the authors of [269] analyzed the convergence bound for FL with non-
IID datasets. Other researches were proposed for uplink and downlink
transmission to improve the convergence time [270, 271, 272, 273].

####  VIII-B3 Client Selection

FL clients execute limited number of training iterations using local update
parameters and only exchange model updates with the aggregating server. The
number of agents is sufficiently large and the bandwidth available for model
update distribution is limited. Thus, it is practical to select the best
subset of clients in the training stage, as client selection policy is
critical to FL in terms of training efficiency and global model quality.
Manifestly, the client nodes generate highly heterogeneous local datasets and
handle different computation capabilities [274], which affects the global
model convergence. The FL framework has to be robust to devise dropout and
anticipate the scenarios where only a small number of participants are left
connected to participate in a training iteration. Several research activities
were devoted to bridge this gap. In [275], the authors design a client
selection framework that flexibly spans the trade-off between convergence
speed and solution bias. They demonstrate that biasing client selection
towards clients with higher local loss achieves faster error convergence. Tang
et al. [276] use the correlations between clients to achieve a faster and more
stable convergence in heterogeneous scenarios. Furthermore, in [277], the
authors propose a client selection mechanism that allows the server to pick
the clients that can participate based on their available resources. The
server decides on the amount of data, energy, and CPU resources required to
take part in each round. In [278], a delay-constrained client selection
framework is proposed for heterogeneous FL in intelligent transportation
systems, aiming to improve the model accuracy, training speed, and
transmission time. Further, reinforcement learning is deployed to improve
client selection performance by involving a reinforcement learning agent that
learns a client selection policy [279]. Also, a multi-armed bandit model is
used in [280] for client selection to boost volatile FL by speeding up model
convergence, promoting model accuracy, and reducing energy consumption.
Alternatively, [281] and [282] propose a client selection method using a
genetic algorithm based on the client’s cost and the model accuracy. In [283],
the authors present the client selection as a game where the clients express
their interest in participating in the FL process.

####  VIII-B4 Fairness & Role Distribution

FL systems are designed to train distributed models using decentralized and
private local training data, which requires to assess the mid/long-term
training robustness and resilience. Most of current techniques combine models
and prioritize them proportionately to the frequency of local samples.
However, the question of role distribution between FL participants remains
unsolved and calls for implementing high fairness, this was examined from the
perspective of ensuring accuracy across clients [284]. Numerous works employ
robust approaches to address fairness, including [285, 286, 287], the authors
tried to improve the overall fairness without compromising the predictive
performance using zero-shot data augmentation, Lyapunov optimization, and
participants reputation, respectively. The research preented in [288]
introduces federated fair averaging to mitigate potential conflicts among
clients before averaging their gradients. In [282], the authors propose an FL
framework that uses a genetic algorithm to ensure fairness and improve the
volume and heterogeneity of data in the FL process. Another approach for fair
client selection is presented in [289], which uses a dynamic multi-criteria
scheme to promote fairness among clients in each round of the FL process.

####  VIII-B5 Data quality

The data quality in FL is a critical factor that directly impacts the
accuracy, reliability, and performance of the trained models. Maintaining high
data quality for FL is a multifaceted challenge that requires careful
considerations:

  * •

_Data heterogeneity:_ In real-world, i.i.d. assumption does not hold as data
sources may experience spatial or temporal correlations, which may lead to
convergence or accuracy inefficiencies and serious fairness issues. Data
heterogeneity is mainly due to variations in data distribution, format, and
quality. Addressing these differences and ensuring representative data from
all devices is crucial reliable training.

  * •

_Data labeling:_ For supervised learning tasks, inconsistent labeling across
devices may lead to biased models and erroneous predictions.

  * •

_Imbalanced data:_ Handling data imbalance in FL is essential to ensure
generalization and fair representation across all classes.

  * •

_Data freshness:_ Ensuring data freshness is crucial to capture recent trends,
patterns, real-time changes, and user behaviors. Meanwhile, network latency,
communication overhead, and battery consumption are to be addressed for
efficient data freshness and communication trade-offs.

  * •

_Data Regulation:_ To comply with data storage/processing location, protection
regulations, and other constraints, FL systems have to consider legal and
ethical aspects in the whole data value chain.

The framework introduced in [290] combines FL and blockchain to ensure high
data quality for complex ML tasks while increasing the accuracy of the trained
models. Moreover, employing pre-processing techniques, local validation, and
rigorous aggregation strategies contribute to maintaining data quality and
fostering more robust and dependable FL models.

###  VIII-C Wireless Challenges for Federated Learning

To train FL algorithms, all participants must transmit their training
parameters over a shared network which require to face several important
communication issues [291]. In this section, we present the main problems
related to the implementation of FL over wireless environments.

####  VIII-C1 Resource Constraints

Wireless devices are usually equipped with limited hardware and software
capabilities, which may drastically affect the local model training efficiency
and the whole FL feasibility. Namely, the FL may experience a loss of
efficiency due to the following specific aspects:

  * •

Computational power: Most of mobile handsets and IoT devices have limited
computational capabilities, which affects their ability to perform complex
model updates and heavy local training. This way, the so called stragglers
with low processing capabilities may lower the training quality and slow the
overall federated learning process. Therefore, it is fo paramount importance
to elaborate policies to handle stragglers, such as strategic redundancy or
per-client weighting.

  * •

Memory limitations: The low amount of memory installed on devices restricts
the size of the models they can handle and increase the processing time.
Therefore, only lightweight model training and/or low-moderate accuracy may be
supported.

  * •

Wireless context: Transmitting model updates and gradients over wireless
requires efficient bandwidth allocation to deal with the channel variability.
Devices suffering from uncontrolled/unpredictable wireless context may
experience poor communication experience, leading to unbounded delays and
communication overhead in particular under large models or frequent updates.

  * •

Energy Considerations: Running mobile handsets and IoT sensors faces a serious
energy constraints due to limited battery life. Indeed, intensive model
training tasks may lead to rapid battery depletion, affecting the FL
robustness and user experience afterwards. Then, it is of utmost importance to
design energy-efficient model update mechanisms and green communication
strategies to sustain a satisfactory learning experience.

Abovementioned limitations impact the effectiveness, efficiency, and
feasibility of implementing FL over wireless network systems. Numerous works
(e.g., [292, 293]) explored techniques such as model compression, federated
optimization strategies, and adaptive learning algorithms tailored to
resource-constrained devices to balance the model accuracy and resource
efficiency while enabling effective participation of a diverse range of
devices in the FL ecosystem.

####  VIII-C2 Federated Learning Over Wireless

Wireless links introduce additional challenges when transmitting model updates
and data from/to participating devices to/from the central server in an FL
framework. Symbol errors introduced by the unreliable nature of the wireless
channel impact the quality and correctness of the FL updates among users and
affect the performance of FL algorithms, as well as their convergence speed.
To address the inherently unreliable connectivity in FL, the authors of [294,
295] consider error correction coding, adaptive modulation and coding schemes,
robust aggregation algorithms, and the use of redundant data transmission.
These approaches aim to ensure that the FL process continues running
effectively even under faulty wireless communications.

####  VIII-C3 Over-the-Air Data Aggregation

Over-the-air computation offers the advantage of low resource consumption
since the BS only needs to handle functions uploaded by users rather than
dealing with individual data. FL implementing over-the-air aggregation enables
users to share the same spectral resources, resulting in communication
efficiency enhancement [296]. However, several works such as [297, 298] have
examined the impact of the channel time variations on the convergence of the
FL with over-the-air aggregation, and show that the resulting undesired
interference has only limited destructive effects, which do not prevent the
convergence of the learning algorithm. Furthermore, other issues should be
addressed such as model distortion caused by channel fading, inefficient over-
the-air aggregation of locally trained models with significant data
imbalances, and the constrained availability and verification of individual
local models.

###  VIII-D Hot Topics and Insights

In this section, we provide a broad overview of the hot topics that still need
addressed for meaningful FL. We also outline several directions of future work
with continuingly high interest for a wide range of research communities.

####  VIII-D1 FL and Edge Computing/Networking

Combining FL and edge computing promises to handle the massive amount of data
securely gathered from ubiquitous mobile devices. Edge computing refers to
situations in which user nodes have computational and/or storage capability
but need edge help for coordination or to offload the most computationally
intensive activities. FL has long been recognized as an excellent match for
edge-hosted applications, and numerous research efforts have aimed to make it
as efficient as possible [299]. Nevertheless, the interaction between FL and
edge computing/networking is becoming an open battlefield with several issues
to solve. First, the nodes connect to the edge-based server using different
radio access technologies, which influences the delay experienced while
delivering model updates. Second, heterogeneous mobile clients with different
capability also affects the FL performance. Therefore, the set of
participating clients in the FL process needs to be strategically chosen to
communicate effectively. Third, during the exchange of model updates, several
clients participate by contributing their on-device training data, and it is
difficult to prevent malicious clients from sending fake data to poison the
training process which in turn poisons the model or the received model
gradient parameters.

####  VIII-D2 FL for 6G applications

Future 6G applications are expected to generate and handle large amounts of
data, such as extended reality (XR), mixed reality (MR), wireless brain-
computer interactions, hologram communications, tactile internet, mURLLC,
XURLLC, and other use cases that require high QoE, astonishing reliability and
extremely low latency [300]. Federated ML schemes are highly recommended to
obtain greater learning and secure communication between distributed devices
[301]. However, new concerns arise when applying FL for emerging technologies,
essentially the convergence analysis that is affected by the large propagation
attenuation in THz and the optimization of the parameters for quantum key
distribution to name a few.

####  VIII-D3 FL meets Blockchain

Blockchain technology combines encryption with distributed computing to
improve the privacy of decentralized networks while cutting down the need for
a central authority. In [302, 303], it has beeb shown that implementing FL and
blockchain helps automating energy transfers and allows autonomous energy
providers to safely trade value [304]. Additionally, game theoretic tools may
help FL to substantially reduce communications among distributed wireless
networks and address the fundamental problems of large-scale distributed and
privacy-preserving learning algorithms [305, 306]. Local federated learners
are the players and the aggregation of the received gradients in a central
server is the mean-field game effect. Adopting incentive mechanisms via
blockchain and game theory helps distributed systems exploring the user’s
habits and preferences, assessing and improving the relevance of online search
engines.

####  VIII-D4 Federated Distillation

Despite being communication-efficient, FL still requires the transmission of
extensive models from large number of clients through the air. FL has well-
known limits, such as:

  * •

Clients must implement the same model architecture;

  * •

Transmitting the huge model weights and updates implies high communication
costs;

  * •

Parameter-averaging aggregation schemes perform poorly due to client model
drifts.

Knowledge distillation helps overcoming limitations of the parameter-averaging
technique [307]. Federated distillation is a process of distilling knowledge
from a larger central model to a smaller model on edge devices [308]. Within
this process, knowledge is transferred between models by having each model
acting as a teacher to the other models. Each model shares its knowledge with
the other models, allowing the models to learn from each other and improve
their performance [309]. Such a mechanism leads to improved accuracy as more
data is implicitly used for training. It also offers improved privacy and
security as data is not shared. Moreover, federated distillation exhibits a
reduced bandwidth usage due to the distributed training. Likewise, the authors
of [310] propose to distill knowledge in the FL process, with key idea relying
on exchanging soft targets instead of transferring the model parameters
between server and clients. The authors showed that their solution can reduce
both communication overhead and computation costs.

####  VIII-D5 FL for Semantic Communications

Semantic Communication (SC) is a new paradigm, expected to be a major part of
6G and beyond. It allows to achieve more efficient data processing and
transmission considering user expectation and application requirements as well
as the semantic/meaning of the data being sent. SC combines natural language
processing, AI, and ML to create a powerful and dynamic communication system
able to understand the context of the data being processed. Indeed, it allows
to build more secure, reliable, and energy-efficient communication networks.
In addition, semantic communication is expected to reduce the latency and
complexity of data processing, allowing for more efficient data transmission
and improved user experience [311]. This way, SC is anticipated to support
novel applications and services, such as intelligent virtual assistants, smart
robots, and autonomous vehicles. However, some SC processes such as semantic
detection, knowledge modelling, and coordination, are resource-hungry and may
show inefficient behaviors, especially for communication between a source and
a destination.

FL framework addresses this challenge by training a shared model to process
the common semantic knowledge without exposing local semantic data from each
edge server. Instead, the edge servers collaborate by exchanging intermediate
model training results. In [312], a federated ML is used to support resource-
efficient semantic-aware networking. The proposed solution allows each user to
offload computationally intensive semantic encoding and decoding tasks to edge
servers, while it protects its own model-related information by coordinating
via intermediate results.

####  VIII-D6 FL and Energy Efficiency

Energy efficiency is critical to build sustainable 6G and next generations of