# Decentralized Federated Learning:
A Survey and Perspective

Liangqi Yuan, , Ziran Wang, , Lichao Sun, , Philip S. Yu, , and Christopher G.
Brinton Manuscript received May 03, 2024.L. Yuan, Z. Wang, and C. G. Brinton
are with the College of Engineering, Purdue University, West Lafayette, IN
47907, USA (e-mail<EMAIL_ADDRESS><EMAIL_ADDRESS>cgb@purdue.edu).L. Sun is with the Department of Computer Science and
Engineering, Lehigh University, Bethlehem, PA 18015, USA (e-mail:
lis221@lehigh.edu).P. S. Yu is with the Department of Computer Science,
University of Illinois at Chicago, Chicago, IL 60607, USA (e-mail:
psyu@uic.edu).

###### Abstract

Federated learning (FL) has been gaining attention for its ability to share
knowledge while maintaining user data, protecting privacy, increasing learning
efficiency, and reducing communication overhead. Decentralized FL (DFL) is a
decentralized network architecture that eliminates the need for a central
server in contrast to centralized FL (CFL). DFL enables direct communication
between clients, resulting in significant savings in communication resources.
In this paper, a comprehensive survey and profound perspective are provided
for DFL. First, a review of the methodology, challenges, and variants of CFL
is conducted, laying the background of DFL. Then, a systematic and detailed
perspective on DFL is introduced, including iteration order, communication
protocols, network topologies, paradigm proposals, and temporal variability.
Next, based on the definition of DFL, several extended variants and
categorizations are proposed with state-of-the-art (SOTA) technologies.
Lastly, in addition to summarizing the current challenges in the DFL, some
possible solutions and future research directions are also discussed.

###### Index Terms:

Federated learning, decentralized learning, network, privacy preservation,
internet of things (IoT).

##  I Introduction

Federated learning (FL) is a decentralized learning paradigm with natural
privacy-preserving capabilities, which shares only model weights instead of
user data [1]. Federated learning was first proposed by Google researchers in
2016 [2] and was applied to build a language model collaboration framework on
Google Keyboard to learn whether people clicked on recommended suggestions and
contextual information [3]. In 2020, Google researchers expanded the concept
of FL to federated analytics[4, 5, 6, 7, 8, 9, 10], extending from learning
tasks to collaborative computing, data analysis, and inference, further
deploying it within Google Keyboard. FL has demonstrated its excellent
capabilities in various areas, including intelligent transportation, internet
of things (IoT), healthcare, manufacturing, agriculture, energy, remote
sensing, and more [11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,
26, 27, 28]. FL also breaks geographical limitations allowing efficient
collaboration worldwide [29]. Researchers employed FL to aggregate data from
20 institutes worldwide to train a universal model to predict clinical
outcomes of COVID-19 patients [30]. FL improves the generalization capability
of the model to include knowledge of diverse data. Other researchers have also
used FL to aggregate data from 71 sites for rare cancer boundary detection,
which greatly enriches the dataset to support research on rare diseases [31].

Figure 1: Comparative analysis between centralized FL and decentralized FL
across various performance metrics. Each axis represents a metric with the
plotted values indicating the relative strength of the respective FL approach
in that domain.

Traditional FL focuses on the decentralized learning and centralized
aggregation paradigm established by data parallelism. Data parallelism refers
to the situation where the raw data of the clients is generated in parallel
locally, and this raw data is neither sent out nor visible to others. Each
client trains a model based on its local data and then communicates the model
parameters with the server to ensure the effective integration of learning
results from each client and obtain a global model. An FL taxonomy refers to
the number and nature of clients participating in the learning network,
including cross-silo and cross-device FL frameworks [1]. The clients in cross-
silo FL usually are different organizations, research institutions, data
centers, etc., which may have more reliable communication, computational
resources, and a large amount of data. The clients in cross-device FL are huge
mobile or IoT devices, which can encounter potential bottlenecks in
communication and computation. Another FL taxonomy is considered for
differences in data distribution among clients, including horizontal,
vertical, and transfer [32]. In horizontal FL, clients have more similar
sample features and fewer identical users. Clients in vertical FL have more
similar users and fewer similar sample features. Federated transfer learning
clients have neither many similar sample features nor similar users.

Figure 2: Illustration of local learning, centralized learning, CFL, and DFL.
(a) Clients are trained with local user data only. The clients neither share
raw data nor communicate with each other. (b) After clients send the user data
packets to the server, the server trains a general model using all the data.
The generalized model is then shared with all clients. (c) Clients send the
locally trained model parameters to the server. The server aggregates all the
local models and then transmits the aggregated global model parameters to all
the clients. (d) Clients share their locally trained model with other clients.
Subsequent clients then continue to learn, personalize, and adapt the model
locally, while also exchanging and propagating the model parameters that
possess local knowledge.

In this paper, we present a thorough investigation into decentralized FL (DFL)
and offer novel perspectives on its taxonomies. Distinguishing from the
conventional centralized FL (CFL) that relies on a central server for
aggregation, we specifically focus on the less-explored DFL framework, which
operates independently of a central server. Fig. 1 illustrates a comparison
between CFL and DFL across nine key evaluation metrics, which are the focal
points of current state-of-the-art (SOTA) research and worthy of further
investigation [33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47,
48]. Given their inherent characteristics, CFL and DFL each exhibit unique
advantages in different applications.

Fig. 2 shows the illustration of local learning, centralized learning, CFL,
and DFL. In the local learning strategy, the user data and trained model of
each client are only used locally, and they do not communicate with any other
clients or servers, as shown in Fig. 2(a), but this may lead to overfitting.
Alternatively shown in Fig. 2(b), the centralized learning strategy involves
the transmission of raw data in the communication between clients and the
server, which consolidates and centralizes the learning process but does not
guarantee the privacy of the users. Both of these strategies are often used by
researchers as baselines to compare with FL.

CFL is a centralized structure where a server will communicate, coordinate,
and manage all clients. Fig. 2(c) shows the communication between clients and
the server. Clients learn on local data and then upload the trained model
parameters to the server. The server aggregates the local models and then
shares the global model with the clients. The idea is that all clients
contribute to one global model, and the one global model is applied to all
clients. For CFL, clients only share the trained local model parameters with
the server but not the users’ raw data. FL not only protects users’ privacy
and improves learning efficiency, but also saves communication resources when
the model size is much smaller than the data size.

DFL is a decentralized structure in which clients communicate and share model
parameters with each other without any server. There are relevant designations
in the recent literature, such as peer-to-peer FL [49], server-free FL [50],
serverless FL [51], device-to-device FL [52], swarm learning [53], etc. Fig.
2(d) shows clients communicating directly with other clients without server
coordination. Since there is no unified coordination and configuration of
servers, the communication network between clients is more diverse. For the
DFL discarding the server is considered to be more customizable, which can
further save communication and computational resources with higher confidence
in diverse variants. The pointing and peer connections in the communication
network are adaptively configured and changed according to the scenario, which
is one of the advantages of DFL. In addition to the typical line, ring, and
fully connected peer connection types, it is conceivable to connect based on
geographical neighbors, the similarity of clients, communication protocols,
etc.

The concept of DFL was first proposed in the year 2018 [54]. As of June 1,
2023, a search on Google Scholar yields 1,350 results related to DFL, with a
substantial number of 652 contributions coming from the year 2022 alone. The
research associated with DFL exhibits a persistent exponential growth
trajectory. DFL has received extensive attention as an emerging framework [55,
56, 57, 58, 59, 60]. The most significant advantage of DFL is that it
eliminates the communication resources needed for the server as an
intermediary step and the high bandwidth requirements associated with it. Xu
et al. [61] listed DFL, model compression, selective client communication, and
low communication frequency as four ways to reduce communication costs. Lian
et al. [62] demonstrated the advantages of decentralized learning over
centralized learning, especially since the number of clients in decentralized
learning is proportional to the speedup.

TABLE I: Comparison of Related Surveys of Decentralized Federated Learning

Survey | Time | Focused DFL Topic | Application Scenarios | Order | Protocol | Topology | Paradigm | Variability
---|---|---|---|---|---|---|---|---
[1] | 2021 | Introduction | ✗ | ✗ | ✗ | ✗ | ✗ | ✗
[63] | 2021 | Introduction | ✗ | ✗ | ✗ | ✗ | ✗ | ✗
[64] | 2021 | UAV |  | ✗ | ✗ | ✗ | ✗ | ✗
[65] | 2021 | IoT |  City | ✗ | ✗ | ✗ | ✗ | ✗
[66] | 2022 | Blockchain | ✗ | ✗ | ✗ | ✗ | ✗ | ✗
[67] | 2022 | Survey | ✗ | ✗ | ✗ | ✗ | ✗ | ✗
[68] | 2022 | Survey & Reflection |  IoT Finance | ✗ | ✗ | ✗ | ✗ | ✗
[69] | 2023 | Tutorial | ✗ | ✗ | ✗ | ✗ | ✗ | ✗
[70] | 2023 | Survey | ✗ | ✗ | ✗ | ✗ | ✗ | ✗
[71] | 2023 | Topology | ✗ | ✗ | ✗ | ✓ | ✗ | ✓
[72] | 2023 | CAV |  | ✗ | ✗ | ✓ | ✗ | ✗
[73] | 2023 | Survey & Tutorial |  | ✗ | ✓ | ✓ | ✗ | ✗
[74] | 2024 | Security and Privacy | ✗ | ✗ | ✗ | ✗ | ✗ | ✗
Ours | 2024 | Survey & Perspective |  | ✓ | ✓ | ✓ | ✓ | ✓

✓ Included, ✗ Not mentioned.
: Connected and Automated Vehicles (CAVs), : Healthcare, : Industrial IoT
(IIoT), : Mobile Services,

: Unmanned Aerial Vehicle (UAVs) and Satellites, : Social Networks, :
Artificial General Intelligence (AGI)

Figure 3: Roadmap for this perspective paper.

Although FL has shown unprecedented advantages, most of the current research
has been limited to CFL. DFL, as an essential branch in FL, is proliferating
and offering benefits over CFL. Recent surveys have focused more on CFL, with
less attention given to DFL [75, 61, 76, 77]. Furthermore, there is a lack of
a comprehensive, in-depth, and insightful survey that establishes the logic of
building a DFL system, including iteration, communication protocol, network
topology, paradigm, and more. This paper begins with a review of CFL,
summarizing its challenges and various extended variants as potential
solutions that can be compared and analogized with DFL. As an emerging field,
this survey aims to fill gaps in the DFL survey literature by covering
perspective papers that are currently not included. It systematically
integrates and categorizes the SOTA research in DFL. A detailed and
comprehensive comparison of our survey with other related DFL surveys can be
found in Table I.

The contributions of this paper are:

  * •

We provide a description of CFL, summarize the challenges, and offer a
detailed introduction to the various variants, their roles, addressed issues,
and advantages.

  * •

We systematically define and describe five taxonomies of DFL, including
iteration order, communication protocol, network topology, paradigm proposal,
and temporal variability. To the best of our knowledge, this is the first
comprehensive and insightful perspective paper for DFL.

  * •

Based on the network topology, we propose and envision five variants of DFL to
categorize the recent literature, anticipate potential application scenarios,
and highlight the advantages.

  * •

We summarize five current challenges, possible solutions, and future research
directions for DFL.

The presentation of this paper is summarized as shown in Fig. 3. Section II
reviews the history of CFL, the existing challenges, and some variants as
potential solutions. Section III provides the definitions and descriptions of
DFL communication protocol, network topology, and paradigm proposal. Section
IV demonstrates several variants in DFL, followed by Section V analyzing the
challenges of DFL. Finally, Section VI provides a summary of this paper.

##  II Review of Centralized Federated Learning

McMahan et al. [2] proposed the first mature and most popular FL algorithm,
federated averaging (FedAvg). At each communication round, clients upload
their trained local models to the server, and the server weighted averages all
local models according to the number of client samples. Based on FedAvg,
various derivation and optimization schemes exist to address the challenges in
the FL algorithm [78, 79]. Li et al. [80] developed an advanced algorithm
FedProx to penalize the bias of the local model to the global model by a
proximal term. The advantage is to limit the significant variance and unstable
convergence of local models due to overfitting on clients with system
heterogeneity. Wei et al. [81] took into account the privacy leakage concern
of model parameters uploaded by clients in FL and proposed to improve the
differential privacy by adding noise before the client sends it to the server
for aggregation. Also, the game trade-off between FL convergence and privacy
preservation and the optimal communication rounds were highlighted.

Although the diverse derivations that exist complement the performance of FL,
there are undeniable drawbacks, such as a single point of failure (SPoF) on
the server. In this section, after presenting some of the challenges and
limitations of the server, we show some variants of the solution and SOTA
technologies.

###  II-A Challenges in Centralized Federated Learning

For CFL, the server takes on many responsibilities and challenges, with large
service providers, such as large organizations and research institutions,
playing the role of server. While these large providers have unparalleled
resources compared to small workshops, there are some concerns here as the
number of clients grows endlessly [82].

1) Client Heterogeneity predominantly stems from three latent factors:
individual, group, and systemic heterogeneity [80, 83, 84, 85]. Individual
heterogeneity arises from differences inherent to each sample or individual,
such as variances among patients in a hospital setting. Group heterogeneity is
rooted in shared characteristics among subsets of samples, such as patients of
different age groups, regions, or medical backgrounds. Systemic heterogeneity
originates from variations introduced during data collection by the system,
which can include discrepancies from different equipment, clinical practices,
or data collection personnel.

2) Communication Resource is limited on both the server and client sides [86,
87]. Although FL has dramatically reduced the consumption of communication
resources by sharing only model parameters instead of user raw data,
communication resources are a serious problem considering the large number of
parallel clients (up to one billion). In particular, when delays in
communication cause the server to wait for clients with communication
problems, it can also cause the whole FL framework to become highly
inefficient. Some FL communication proposals have been proposed to improve
communication efficiency [88, 89, 90, 91].

3) Computational and Storage Resource on the server side are also challenged
[92, 93, 94]. The server needs to store and aggregate the models of these
billions of clients. Even though lightweight models are emerging recently [95,
96], the need to compute and store model data can easily reach petabytes in
size [97]. Besides the current version of the massive local model,
subconditionals and versioned storage of the global model may also be
required. Additionally, as clients demand real-time processing of a large
volume of inference tasks, this places high demands on the computational
resources required during inference [98].

4) Fairness, Security, and Trustworthy have always been crucial concerns in
CFL, with these factors significantly impacting the system’s overall
reliability, user confidence, and data integrity. A series of questions
related to security and trust form the chain of suspicion: whether the server
aggregation model is reasonable, whether the global model will have high
performance across all clients, whether the global model is validated, how to
use the global model securely, and whether the server is secure from attacks
[99]. For security issues, there are different directions of research,
including malicious attacks [100], data poisoning [101], anomaly detection
[102], and privacy protection [81]. For trustworthy [103], fairness [104],
incentive [105], and interpretability [106] in FL are also worthy research
directions.

5) Unreliable Connection in FL can stem from factors such as unreliable
communication conditions, malicious attacks, or server malfunctions, leading
to delays, packet loss, or noise in model transmission [107, 108, 109]. As all
clients typically communicate with a central server, an SPoF can halt the
entire system’s update process. Although employing multiple edge servers can
distribute the risk of SPoF, it may still cause the system segments connected
to an affected edge server to become unresponsive. [110, 111] have explored
using blockchain technology to mitigate SPoF by replacing the central server
role, but this approach diverges from the conventional centralized FL model.

###  II-B Variants of Centralized Federated Learning

The network variants and extensions of CFL are designed to address the above
challenges and adapt to different real-world application scenarios.

1) Hierarchical FL features a classic and popular client-edge-cloud
architecture, where model parameters are infrequently transmitted between the
edge and the cloud, effectively reducing communication overhead [112, 113,
114]. It usually perform additional aggregations by setting up additional edge
servers [115], which aim to spread the communication [116] and computing
pressure and reduce the impact of SPoF. These additional edge servers are
geographically closer to clients, resulting in less communication resource
consumption and lower latency [117, 118, 119]. After one or more edge server
aggregations, the edge servers then upload the edge global model to the cloud
for aggregation into a global model. In addition to communication
optimizations, the geographic proximity of edge servers to clients may also
lead to better adaptation of edge servers to the connected clients. Edge
servers and connected clients can be considered geographically personalized
clusters. For example, by assigning edge servers to states in the United
States, the state edge servers can be more personalized to the state’s user
scenarios and user habits, such as weather, number of users, time zone,
ethnicity, age distribution, etc.

2) Personalized FL can be classified into two categories, i.e., global model
personalization and personalized model architecture [120, 121, 122]. Global
model personalization usually starts with a global model, and then the client
personalizes this global model to fit the local user. Personalization is the
behavior of the client independent of the server, such as federated transfer
learning to transfer global model knowledge locally [123, 124]. The
personalized model architecture changes the traditional FL architecture to
develop a personalized model with user knowledge, which is the behavior of the
server. A famous architecture is clustered FL that has been of interest to
researchers [125, 83]. The client model in the personalized FL framework is
closer to the user, so it is known for its high accuracy and confidence. In
particular, it is a highly effective solution for non-independent and
identically distributed (non-IID) data. When the aggregated global model
deviates from the user, personalization can transfer the model and adapt it to
different heterogeneities.

3) Split FL splits the model for learning, where the server is responsible for
some model layers [126, 127, 128]. The only data sent by the client to the
server are the hidden representations and/or gradients in the cut layer of the
model. The client not only shifts part of the learning task to the server but
also does not share the user data. Compared to traditional FL, the split FL
framework has similar accuracy and communication efficiency with a lower
learning burden on the client side and more robust privacy protection.
However, split FL is still in its early stages and has significant
limitations, such as the need to consume more communication resources.
Especially the presence of SPoF on the server can have even greater
consequences.

4) Graph FL is particularly effective for applications involving graph-
structured data, such as social networks, transportation networks, molecular
structures, etc [129, 130]. This effectiveness stems from the fact that
clients possess local or independent graph data, which includes rich
relational information about nodes and edges. More specifically, Graph FL
encompasses three levels, depending on the level of detail clients have about
the graph data. These levels include multiple graphs, different parts of
multiple graphs, and parts of a single graph [131]. In addition to employing
graph neural networks for graph data in FL, some researchers have also
considered topological graphs between clients [132]. These topological graphs
can be established based on various factors, exploring network connectivity
conditions between clients and the server, data and model availability of
clients, as well as similarities and data generality among clients [133, 134,
135].

5) Asynchronous FL is designed to overcome the limitations of FL frameworks
that require clients to synchronize model updates. This approach aligns well
with the real-world scenarios in which the client’s data updates and model
training vary significantly. In these systems, heterogeneous clients have
different amounts of data and computational resources and can train and update
their local models at any time without the server having to wait for all
clients to synchronize [136, 137]. Asynchronous FL is particularly suitable
for environments with dynamic changes, a large number of clients, and a wide
distribution, such as mobile devices, which may conduct model training during
idle times rather than during routine user interactions [138, 139].

Variants of CFL currently exist with exotic frameworks that may include
single, multiple, sub, and master servers to optimize and target different
problems. For example, Zhang et al. [140] proposed the (Com)2Net, a large-
scale distributed computing framework capable of spanning space-air-ground,
end-edge-cloud, and multi-data center environments, facilitating ubiquitous
connectivity and collaborative computing in FL. In addition to various
variants, a popular approach is to assemble various variants of the FL
framework to target multiple issues [141].

###  II-C Definition of Client and Server

This paper proposes a new taxonomy that focuses on the roles played by
communication endpoints in FL. We argue that the roles of edge devices,
compute clusters, institutions, and organizations are relative rather than
absolute. For example, a university may act as a central server when managing
edge devices within its campus, but it should be considered as a client when
communicating with other universities. The classification of a role as a
client can be determined by whether it generates raw data and stores the data
locally. For instance, a healthcare institution both generates clinical data
for patients and retains it in its own database without sharing it with other
institutions. Additionally, clients and servers are not mutually exclusive
roles. A healthcare institution, for example, can act as both a client and a
server [31]. It generates raw patient data and performs local training while
also serving as a server by receiving model parameters from other healthcare
institutions, aggregating them, and sharing the updated model. In practice, it
can be considered as a star topology network in DFL.

The emphasis on a star topology network instead of CFL is driven by the more
pressing issues of fairness and trust in FL systems when an institution
simultaneously possesses raw data for local training and assumes the role of a
server for aggregation. Ensuring that this institution does not favor its
local data during aggregation poses an open question. As the saying goes, One
cannot be a judge, jury, and executioner. Similarly, an institution should not
act as both a client and a server. However, this situation is more common in
the real world. On one hand, institutions with more raw data have greater
influence and often initiate tasks. On the other hand, institutions with more
data also tend to have more abundant computing resources, making them better
suited for the server role. In today’s world, where large institutions possess
more resources and hold greater influence, addressing the interests, privacy,
and fairness of non-representative clients is a challenge.

TABLE II: Definitions and Descriptions of DFL Taxonomy Taxonomy | Category |  Description
---|---|---
Iteration Order | Sequential |  Clients are synchronized to communicate one by one in a certain order.
Random |  Clients are synchronized to communicate one by one in a random order.
Cycle |  Clients are synchronized to communicate one by one in cycle.
Parallel |  All clients communicate asynchronously.
Communication Protocol | Pointing |  Clients communicate in a specific form of one-peer-to-one-peer.
Gossip |  Clients communicate in a random form of one-peer-to-one-peer, which may be determined by the neighborhood principle, client model version, complete randomness, etc.
Broadcast |  Clients communicate in a form of one-peer-to-all-peers.
| Broadcast-gossip |  Clients communicate in a form of one-peer-to-multiple-peers, which is also a combination form of Gossip and Broadcast.
Network Topology | Line |  Clients communicate in a sequential pointing form.
Bus |  Clients send the model to all clients behind them in order.
Ring |  Clients communicate in a cycle pointing form.
Mesh |  Clients communicate with all other clients.
Star |  Clients communicate only with the central client.
Tree |  Clients communicate only with their central clients.
Hybrid |  Combination of multiple communication topologies.
Paradigm Proposal | Continual |  Client learns directly from the model of the previous client.
Aggregate |  Client first aggregates the models of past clients and then learns on the aggregated models.
Temporal Variability | Static |  Communication architecture will not change.
Dynamic |  Communication architecture may change with external factors, resource-saving purpose, fairness purpose, concept drift, etc.

##  III Taxonomy of
Decentralized Federated Learning

In this section, we begin by analyzing and comparing DFL and other related
designations. Subsequently, we provide a well-organized, clear, and precise
description of the various iterations, protocols, network topologies,
paradigms, and variations in DFL, as presented in Table II. It is worth noting
that the table comprises five distinct taxonomies, which may exhibit
overlapping meanings as well as conflicting aspects, and can also be applied
in a complementary manner. These taxonomies, representing the viewpoints of
the authors, include summarizations of existing literature, extensions of
understanding, and even inferences regarding potential definitions. This
comprehensive approach aims to strengthen the comprehension and categorization
of concepts in the field of DFL.

###  III-A Iteration Order

In general, FL requires multiple iterations to converge, and iteration order
represents the order of each client in each iteration or the way client queues
are formed in DFL. In CFL, clients iterate in a parallel manner, and the order
in which the server receives the client models does not affect the convergence
of the system. However, in DFL, the iteration order of clients will
significantly affect the performance of client models, and we continue to
discuss this issue in depth in Section III-D. Depending on the specific usage
scenario and task requirements, the client iteration order in DFL can be
determined to be sequential, cyclic, random, parallel, dynamic, or other
strategies. The choice of iteration order can impact the convergence and
performance of the system, and it is important to consider the specific
characteristics and constraints of the application when determining the
appropriate order.

###  III-B Communication Protocol

DFL is a network framework for sharing model weights based on the pointing,
gossip, or broadcast protocol, with the goal of obtaining optimal models
across all clients. Pointing is one of the simplest and most straightforward
forms of establishing a communication relationship between two peers in a
unidirectional, one-to-one, and specified form. The algorithms of gossip and
broadcast have been well established for use in networks [142]. Gossip
protocol is essentially a random one-peer-to-one-peer way for clients to
share, disseminate, and learn knowledge in a stochastic communication method
[143, 144]. It is a standard communication protocol in DFL and is already in
its infancy [145, 146]. The broadcast protocol is a one-peer-to-all-peers
approach that allows the client to broadcast its model to all clients [147].

Hybrid protocols are now more popular, with different gossip, broadcast, and
their combined communication structures designed for different scenarios and
constraints. Aysal et al. [148] proposed a method that combines gossip and
broadcast protocols and can be considered as a one-peer-to-neighbor-peers
approach, where the client first broadcasts to its neighbors before gossiping.
Bellet et al. [149] introduced an algorithm that operates the agent
asynchronously and performs broadcast communication between similar clients
with a focus on obtaining personalized local models.

###  III-C Network Topology

DFL networks are inspired by various network topologies, as detailed in
sources such as [150, 151, 152, 153, 154]. Nedić et al. [142] highlighted and
provided convergence proofs for several network structures, including grid,
star, and fully connected topologies. Due to the absence of server-based
adaptation, management, and propagation constraints, DFL networks exhibit a
diverse range of configurations, as illustrated in Fig. 4. Drawing from Graph
FL concepts, DFL networks can form a graph to represent structured
relationships, with clients as nodes and their connections as edges. This
graph-based approach in DFL enables the quantification of dependencies between
clients based on characteristics such as heterogeneity and communication
patterns, thus enhancing learning efficiency and model robustness. Note that
the depicted line segment indicates a connection between clients, which can be
either unidirectional or bidirectional. The data transmitted between clients
do not only comprise their models, but may also include models from previous
interactions. In addition, the computational scope of each client can
encompass both local learning and aggregation.

Figure 4: Illustration of communication network topology.

For CFL, since all client models are hosted on the server, aggregation (i.e.,
averaging of all model parameters) is the mainstream and popular method for
integrating knowledge from all clients. However, for DFL, the situation is
much more complex. First, the network topology is diverse. There are diverse
network topologies in DFL. At each communication round, clients may obey
different protocols to transmit models to one or more other clients. Second,
there are different versions of the model. Except for the synchronous DFL,
there must be different versions of the model for other DFLs. The subsequent
clients in the learning process will have models that incorporate more
knowledge compared to the previous clients. Third, acquiring all client
knowledge becomes more challenging. Without a centralized server for
collaborative management, future clients face difficulties in accessing the
knowledge of all previous clients, except for the immediate preceding clients,
such as Fig. 4(a). Therefore, there is an urgent need for an alternative
paradigm to complement and expand the FL landscape that is not well-compatible
with aggregation.

TABLE III: Paradigms of DFL Paradigm |  Continual |  Aggregate
---|---|---
Intrinsic |  Client receives a single model per iteration. Learning is performed on the received model without aggregation. |  Client receives multiple models per iteration. Learning is performed locally after aggregating the previous models.
Algorithm |  For each client until convergence do: 1. Receive the model from the previous client. 2. Perform local learning on the model. 3. Transmit the trained model to the next client. |  For each client until convergence do: 1. Receive all other client models from the previous client (pointing and gossip) or other clients (broadcast and broadcast-gossip). 2. Aggregate all received models and perform local learning on the aggregated model. 3. Transmit the trained model and other client models to the next client (pointing and gossip) or transmit the trained model to all clients (broadcast and broadcast-gossip).
Advantage |  • Each client involved in learning has a highly accurate, personalized, high-confidence local model. • Compared to CFL, they do not have the same set of issues on the server side, such as aggregation fairness.
• Fewer communication, computation, and storage resources are required. • More simple and straightforward, suitable for all scenarios. |  • More powerful generalization ability on the obtained model. • Stronger ability to update new knowledge generated by the client.
Challenge |  • Model performance strongly depends on the client iteration order. • Appropriate loss function, learning rate, and training epoch, which allows the model to learn the current client’s knowledge while ensuring that the previous knowledge is not forgotten.
• Catastrophic forgetting of past client knowledge. |  • Repetition and overemphasis on learning from past clients.
Network Topology (Take sequential pointing line DFL architecture as an example) |

###  III-D Paradigm Proposal

We introduce an innovative taxonomy of DFL into two paradigms: Continual and
Aggregate. The main differences between these two paradigms lie in the number
of model updates exchanged between clients and whether aggregation takes
place. The distinction between the paradigms also entails variations in other
settings, such as learning rates. The Aggregate paradigm represents the
archetypal FL algorithm, where each client receives the model from other
clients, aggregates these models, and subsequently conducts local learning.
Conversely, within the Continual paradigm, each client receives the model from
merely one peer client and proceeds to learn directly based upon this
particular model. Continual learning [155, 156], or be called incremental
learning, provides a solid and grounded theory for Continual. A number of
concepts and algorithms for federated continual learning are mentioned in the
recent literature [157, 158, 159, 160], which consider the process of dynamic
data collection in the real world while addressing the issues of non-IID data,
concept drift, and catastrophic forgetting. In DFL, the role of continual
learning is more extensive:

  * •

The subsequent client will directly learn on the model of the previous client.
Compared to local learning and Aggregate, the client is able to obtain a more
personalized model while saving communication, computational, and storage
resources.

  * •

In storage-constrained frameworks, clients do not need to retain any
additional model parameter data.

  * •

In computation-constrained frameworks, clients also do not need to consume
additional resources for aggregation calculations.

  * •

The continuous generation of new data by clients is accommodated, and they do
not need to wait for all data to be collected before starting the local
learning process.

  * •

For tasks that may have concept drift, clients are always provided with the
latest version of the model.

Table III analyzes and summarizes the intrinsic, algorithm, advantage,
challenge, and network topology of these two paradigms. The difference between
these two paradigms is illustrated by the example of sequential pointing line
DFL. In the Continual paradigm, the only content delivered to the subsequent
client is the trained model. The subsequent client just continue learning on
this model, as shown in Table III(a). In the Aggregate paradigm, the previous
client transmits not only the trained local model but also all the previous
models. The learning process performed by the subsequent client is divided
into two parts, first aggregation and then learning, as shown in Table III(b).

In order to compare and illustrate the difference between the Continual and
Aggregate paradigms, pointing, gossip, and broadcast, and different network
topologies. Algorithm 1 shows two paradigms in the sequential pointing line
DFL topology and Algorithm 2 shows pointing ring Continual and broadcast mesh
Aggregate DFL. The difference between the Continual and Aggregate paradigms
can be clearly seen in the pre-processing of the client before learning and
the sharing of the model after learning. The additional requirements of the
Aggregate paradigm for communication, computation, and storage have been
highlighted. Under the Aggregate paradigm, the pointing and gossip protocols
require the client to send more model data at once, while the broadcast and
broadcast-gossip protocols require the client to send at a higher frequency.
The ring topology can be seen as a cyclic variant of the line topology, and
both network topologies are widely used by researchers due to their simple and
straightforward structure. The line topology is a sufficient knowledge
learning system for systems that do not generate new knowledge. However, in a
system that is constantly generating new knowledge, the ring topology may be a
more reasonable topology. It is not only able to re-update the knowledge in
the system but also a feasible solution to catastrophic forgetting.

To further illustrate the learning and communication process among clients in
these two paradigms, Fig. 5 demonstrates the learning process from the first
client to the final client in the parameter space. It is important to note
that we actually have several assumptions here. Firstly, the optimal solutions
of the local models of all clients follow a multivariate normal distribution
in the parameter space. Secondly, considering the systemic and statistical
heterogeneity, some clients exhibit significant biases. Thirdly, although
reasonable loss functions and learning rates are chosen, the models are not
always trained to achieve optimal solutions. The communication and learning
processes of the two paradigms, Continual and Aggregate, are as follows.

  1. Step 1)

Both paradigms initiate learning with the same initial model parameters and
obtain the same Model 1 in Client 1.

  2. Step 2)

Both paradigms learn from Model 1 and reach the same Model 2 in Client 2. It’s
worth noting that the Aggregate paradigm is meaningful when there are two or
more aggregated models available.

  3. Step 3)

In the Continual paradigm, Client 3 learns directly from Model 2 to obtain
Model 3, while in the Aggregate paradigm, Model 1 and Model 2 are first
aggregated, and then Client 3 learns from the aggregated model to obtain Model
3. Note that the Continual paradigm is less complex than the Aggregate
paradigm, as indicated by the length of the black arrow.

  4. Step 4)

In the Aggregate paradigm, the model aggregated by Client 4 is closer to the
center of the Normal distribution than Client 3, so it is expected that the
learning process for subsequent clients will be easier.

  5. Step $n$)

When the client is positioned towards the end of the queue, the learning
difficulty in the Continual paradigm becomes random, depending on the
deviation between the previous client and the current client. However, in the
Aggregate paradigm, the learning difficulty is only influenced by the current
client since the aggregated model is expected to be extremely close to the
center of the normal distribution.

Algorithm 1 Sequential pointing line Continual and pointing line Aggregate
decentralized federated learning.

Input: Client set ($C$), training epoch ($E$), initial model ($\omega_{0}$),
loss function ($\mathcal{L}$), learning rate ($\eta$)

Output: Local models ($\\{\omega_{c}|c\in C\\}$)

for $c\in C$ in sequence do

Copy the model from previous client $\omega_{c}\leftarrow\omega_{c-1}$

Aggregate received models
$\omega_{c}\leftarrow\text{Aggregation}\\{\omega_{1},\omega_{2},...,\omega_{c-1}\\}$

for $e=1$ to $E-1$ do

Backpropagate and update the local model
$\omega_{c}^{e+1}\leftarrow\omega_{c}^{e}-\eta\nabla\mathcal{L}$.

end for

Update the local model $\omega_{c}\leftarrow\omega_{c}^{E}$.

Client $c$ sends $\\{\omega_{1},\omega_{2},...,\omega_{c-1}\\}$ and
$\omega_{c}$ to the next client.

end for

Based on the aforementioned assumptions and iterative process, we can make
certain expectations regarding the accuracy, loss, convergence, and
communication complexity of the clients in both paradigms during training. We
come up with the following speculations:

  1. 1.

The learning loss will exhibit periodic oscillations across client iterations
and eventually converge in both paradigms.

  2. 2.

The convergence of learning loss in the Aggregate paradigm is expected to be
more stable. In the Continual paradigm, the learning difficulty depends on the
discrepancy between the previous client and the current client’s local data,
in other words, it depends on the iteration order of clients. Under the
assumption of a normal distribution, the learning difficulty in the Aggregate
paradigm is determined by the heterogeneity of the current client’s data, and
most clients may have similar data distributions.

  3. 3.

The convergence of learning loss in the Aggregate paradigm is also expected to
be faster due to the decreasing learning difficulty as the client iterations
progress. However, this acceleration in convergence is accompanied by an
increase in communication overhead.

  4. 4.

The Continual paradigm requires more communication rounds to achieve
convergence, whereas the Aggregate paradigm incurs greater communication
overhead per round.

  5. 5.

The Continual paradigm exhibits stronger personalization, while the Aggregate
paradigm demonstrates greater generalization. Depending on scenario
requirements, both paradigms can achieve similar performance after convergence
by adjusting weights.

Algorithm 2 Cycle pointing ring Continual and broadcast mesh Aggregate
Decentralized Federated Learning.

Input: Client set ($C$), training epoch ($E$), initial model ($\omega_{0}$),
loss function ($\mathcal{L}$), learning rate ($\eta$)

Output: Local models ($\\{\omega_{c}|c\in C\\}$)

while $c\in C$ in cyclic do $\triangleright$ line vs. ring

Copy the model from previous client $\omega_{c}\leftarrow\omega_{c-1}$

Aggregate received models
$\omega_{c}\leftarrow\text{Aggregation}\\{\omega_{1},\omega_{2},...,\omega_{c-1}\\}$

for $e=1$ to $E-1$ do

Backpropagate and update the local model
$\omega_{c}^{e+1}\leftarrow\omega_{c}^{e}-\eta\nabla\mathcal{L}$.

end for

Update the local model $\omega_{c}\leftarrow\omega_{c}^{E}$.

Client $c$ sends $\omega_{c}$ to the next client and all other clients.

$\triangleright$ pointing vs. broadcast

end while

###  III-E Temporal Variability

The network topology of DFLs has recently undergone a shift from static to
dynamic trends, adapting to the time-varying external environment [161]. The
inspiration for the separation and clustering of network topologies comes from
group behaviors observed in nature, such as fish schools and bee swarms [162].
When a school of fish encounters a predator, the entire school separates to
avoid it. Similarly, in a bee swarm, a small number of scouts can lead the
entire swarm, demonstrating the herd effect. Interestingly, migratory birds
form V-shaped formations during long-distance flights to conserve energy, and
the birds at the front rotate over time to distribute flight fatigue evenly.
In the context of DFL networks, dynamic topologies may exhibit more robust,
fair, and efficient performance compared to static topologies. The
determination of dynamic topologies in DFL networks can be influenced by
various factors, including:

  * •

External Interference. Strong and unbreakable communication barriers, SPoF,
malicious attacks, and other external factors can lead to changes in the
network topology. In order to avoid the failure of the entire network,
topology adjustments and discards are made.

  * •

Communication Resource Saving. Clients have the ability to dynamically select
their neighbors for each communication. By selectively choosing nearby
clients, communication resources can be optimized and saved. Additionally,
clients can dynamically elect the most central client as a leader during each
communication, enhancing the efficiency and effectiveness of communication
within the network.

  * •

Fairness. In order to ensure fairness among clients, a random selection
process is employed for determining the communication target. This helps to
prevent any bias or preference towards specific clients, ensuring equal
opportunities for all participants.

The development of dynamic topological structures based on these factors
shapes DFL networks to facilitate robust, efficient, and fair communication
among clients.

##  IV Variants of Decentralized Federated Learning

In Section III, we provide a comprehensive definition, introduction, and
propose two paradigm for DFLs. In this section, we review the real-world
applications in DFL, with a specific focus on its diverse applications across
various domains and its real-world deployment. Taking inspiration from the CFL
variants and considering the underlying network topologies depicted in Fig. 4,
we propose several viable topology variants for DFL. These topology variants
serve as alternative options for researchers to consider when deploying DFL.
We discuss the advantages and limitations associated with each variant,
enabling researchers to make informed decisions regarding the most suitable
topology for specific usage scenarios.

Figure 5: Illustration of the two paradigms, Continual and Aggregate, for sequential pointing line DFL in the parameter space, showcasing their respective learning and communication processes. The length of the arrow represents both the learning difficulty and the magnitude of the model parameters that undergo changes during learning, which can be measured using the $\ell_{2}$ norm. Shorter arrows are desired as they indicate more accessible, stable, and accurate model learning and convergence. Excessively long arrows suggest that the given loss function and learning rate may not produce the desired model outcome. TABLE IV: Some Inspiring DFLs with Different Protocols, Topologies, Paradigms, and Variants. Literature | Year | Paradigm |  Type |  Highlight
---|---|---|---|---
Chang et al. [163] | 2018 | Continual |  • Sequential pointing line • Cycle pointing ring |  • Introduced system heterogeneity artificially.
Sheller et al. [164] | 2019 | Continual |  • Sequential pointing line • Cycle pointing ring |  • Obtained a conclusion that catastrophic forgetting worsens as the number of clients increases.
Sheller et al. [165] | 2020 | Continual |  • Sequential pointing line • Cycle pointing ring |  • Considered the DFL framework to output a final model approach.
Huang et al. [166] | 2022 | Continual |  • Sequential pointing line • Cycle pointing ring |  • Introduced synaptic intelligence in Continual DFL to effectively improve model stability, especially for sequential pointing line topology.
Yuan et al. [167] | 2023 | Continual |  • Random gossip ring |  • Considered the highly dynamic and random nature of vehicle connectivity in vehicular networks and employed gossip-based communication to simulate this characteristic when deploying Continual DFL. • Provided a comprehensive comparison between CFL and DFL, such as knowledge dissemination mechanism, communication complexity, generalizability, compatibility, overhead, hidden concerns, etc.
Assran et al. [168] | 2019 | Aggregate |  • Cycle broadcast-gossip mesh • Parallel broadcast mesh |  • Performed a comparison of broadcast-gossip and broadcast protocols.
Roy et al. [169] | 2019 | Aggregate |  • Random broadcast-gossip mesh |  • Pre-requested model versions from other clients. • Considered the scenario where the DFL framework outputs a model to a new client.
Pappas et al. [170] | 2021 | Aggregate |  • Parallel broadcast star |  • Proposed a framework that combines star DFL with split learning.
Warnat et al. [53] | 2021 | Aggregate |  • Dynamic pointing star |  • Elected a leader dynamically via a blockchain smart contract that is used to aggregate model parameters.
Shi et al. [171] | 2021 | Aggregate |  • Cycle broadcast-gossip hybrid |  • Analyzed the convergence of broadcast-gossip in a hybrid network.
Chen et al. [172] | 2022 | Aggregate |  • Cycle broadcast mesh |  • Introduced the superposition property of the analog scheme to improve the parallelism of communication, which enables a significant reduction communication rounds.
Wang et al. [173] | 2022 | Aggregate |  • Dynamic parallel broadcast-gossip hybrid |  • Promoted more frequent communication in the central client to achieve fast convergence. • Promoted less frequent communication in the other clients to achieve low communication latency. • The proposed algorithm is applicable and generalized to all hybrid networks.

###  IV-A Real-World Applications

The development of a DFL framework relies on several key factors, such as
relevant application scenarios, sources of information acquisition,
information processing units, and perceptual prediction modules, among others.
With the establishment of the theoretical framework for networks, DFL has been
adopted in various application domains, including vehicles, healthcare,
industrial IoT, social networks, etc.

1) Connected and Automated Vehicles (CAVs) serve as a robust hardware
infrastructure for DFL, leveraging onboard batteries, diverse sensors,
computing units, storage devices, and more. Existing vehicle networking
frameworks, such as vehicle-to-vehicle (V2V), have also laid the foundation
for communication and networking experiences in DFL for CAV [174, 72].
Referred to as V2V FL, this approach enables the exchange and sharing of up-
to-date knowledge among vehicles and has been explored in recent studies [175,
176, 177, 178, 179, 180, 181, 182]. Lu et al. [183] proposed a vehicular DFL
approach with a focus on privacy protection and mitigating data leakage risks
in vehicular cyber-physical systems (VCPS). In their framework, roadside units
(RSUs) are responsible for forwarding vehicle identities, vehicle data
retrieval information, data profiles, data sharing requests, and related
tasks. Once the V2V connection is established through the RSU intermediary,
the model data is directly transmitted to the requesting vehicle.

2) Healthcare Institutions are inclined towards DFL frameworks over CFL due to
their abundant patient privacy data, computational resources, and storage
capabilities. As key stakeholders in healthcare institutions, clinicians play
a crucial role in data collection, model training, data analysis,
characterization, and providing experimental results and solutions. Unlike
traditional server-centric approaches, clinicians have the flexibility to
observe, analyze, fine-tune, and match models manually, offering more control
and adaptability. Healthcare institutions widely employ DFL frameworks in
various studies [163, 164, 165, 166, 61, 184, 185, 186]. Warnat-Herresthal et
al. [53] introduced a DFL framework called Swarm Learning, which addresses
four use cases of heterogeneous diseases, including COVID-19, tuberculosis,
leukemia, and lung pathologies. This framework incorporates a blockchain smart
contract for enhanced security and dynamically selects a leader for
aggregating model parameters in each iteration.

3) Industrial IoT (IIoT), as a cornerstone of Industry 4.0, greatly benefits
from DFL, which offers robust and scalable solutions tailored to the
complexities of modern manufacturing environments [187, 188, 189]. IIoT
requires enhanced robustness, and DFL improves system resistance to SPoF,
which is particularly crucial for devices on production lines [190]. The high
autonomy of DFL makes it well suited for industrial settings with diverse
physical conditions and operational environments. Moreover, IIoT can rely on
the scalability of DFL to easily adapt to geographically dispersed production
sites and dynamically accommodate new industrial devices into the DFL system.
Du et al. [191] designed a DFL framework for IIoT, where each client exchanges
model parameters only with neighbors using a broadcast-gossip communication
protocol to achieve model consensus. To enhance the efficiency of the gossip
protocol and reduce communication overhead, they also consider the topology of
the entire client network to facilitate asynchronous model exchanges between
clients.

4) Mobile Services based on IoT devices provide a significant application
scenario for DFL, leveraging the capabilities of smartphones, laptops,
tablets, etc. These mobile IoT devices are equipped with various sensors, such
as global positioning system (GPS), inertial measurement unit (IMU), cameras,
sound sensors, and magnetic sensors, enabling them to acquire diverse sources
of information. Unlike the relatively fixed connectivity of CAVs, mobile IoT
devices offer more flexible systems and platforms to support a wide range of
applications. In recent studies, DFL frameworks have been developed
specifically for mobile IoT devices, aiming to leverage their computational
power and sensor capabilities [192, 193]. While the traditional example of
CFL, such as Google mobile keyboard prediction, is well-known [3], the
transfer of such applications to DFLs is of great interest. For instance,
building DFLs among individuals with similar professions, such as doctors,
lawyers, or engineers, can enable personalized word recommendations tailored
to their specific needs. Belal et al. [194] developed a smartphone-based DFL
personalized recommendation system for New York City attractions and movies.
By sharing model parameters with neighbors who have similar interests, the
system achieves higher hit rates and faster convergence, enhancing the
recommendation accuracy and user experience.

5) UAVs and Satellites, operating in dynamic computing environments, possess
vast amounts of sensitive data for remote sensing, target recognition, and
military-related tasks, under the constraints of limited resources, making
them well suited for the advantages of DFL [195, 196, 197]. For mobile UAVs
and satellites, bandwidth is a valuable resource. One potential solution is
that using a DFL framework based on broadcast gossip tailored to dynamic
geographic locations can significantly reduce bandwidth requirements and the
consumption of communication resources [64]. Moreover, due to their dynamic
nature and highly variable data, DFL can also enhance real-time
responsiveness, adaptability, and efficiency of task execution. Han et al.
[198] proposed a DFL framework that orchestrates satellite constellations,
aggregating models within a satellite cluster and relaying models to other
satellites via inter-satellite links, particularly considering the dynamic
scenarios of low earth orbit satellites with varying orbits.

6) Social Networks, as large-scale knowledge graphs, contain various users as
nodes, content, and connections, making DFL highly effective for handling a
widely dispersed and personalized user base, where each user is connected only
to their neighbors [199]. Use cases in social networks include sentiment
analysis, recommendation systems, publication systems, and influence analysis,
among others [130, 200, 201]. Users in social networks come from diverse
backgrounds, such as teachers and doctors, but may share common interests in
topics like comics, leading to frequent interactions. The connections within
and between user groups vary in proximity, thus emphasizing the need for a
personalized and scalable DFL approach. Chen et al. [202] developed a DFL
framework for social networks that establishes a user data structure with
affine distributions. This structure helps capture the heterogeneity among
users and reduces the loss associated with independently and identically
distributed data.

7) Artificial General Intelligence (AGI), as one of the popular research areas
today, represented by large language models (LLMs) like ChatGPT, has the
potential to fundamentally change our lives [203]. The training of these
models relies on vast amounts of computational resources and data, which can
benefit from the collaborative learning paradigm of DFL to facilitate
cooperation among different data centers [204, 205]. Unlike other application
areas discussed earlier, such as healthcare, AGI models feature billions or
even tens of billions of model parameters and are intended for general use
cases, not just confined to a specific application. This poses unique
challenges for the application of DFL in AGI. Qin et al. [206] proposed a
method using FL for full-parameter fine-tuning of billion-scale LLMs,
employing zeroth-order optimization with a random seed subset, reducing
communication requirements to just a few random seeds and scalar gradients,
totaling only a few thousand bytes. Meanwhile, Gao et al. [207] only proposed
a purely theoretical design for decentralized LLMs. Therefore, the use of DFL
for AGI remains a nascent field requiring further research [208].

(a) Line/Ring

(b) Mesh

(c) Star

(d) Hybrid

Figure 6: Illustrations of imagined DFL network topologies in the real world:
(a) line/ring, (b) mesh, (c) star, and (d) hybrid. The red dots represent
clients, which can be universities, institutions, or organizations in some of
the major cities in the world (determined by population). The blue lines
depict the communication network among these clients. Depending on the chosen
topology, the communication networks exhibit different communication
distances, number of communication links, complexity, and other
characteristics.

###  IV-B Variant: Line

The base variant of DFL can be considered as a sequential pointing line,
depicted in Fig. 4(a) and Fig. 6(a). This topology serves as the simplest and
most straightforward illustration and comparison in this paper, as
demonstrated in Table III(a), (b), and Algorithm 1. The line variant is
frequently used as a baseline for comparison due to its ease of
implementation, intuitiveness, and efficiency [163, 164, 165, 166]. However,
it has notable limitations, such as the inability to accommodate continuous
learning of new knowledge within the system, the risk of catastrophic
forgetting in the Continual paradigm, or redundant and excessive learning in
the Aggregate paradigm, as well as limited generalization ability for starting
clients and the vulnerability to a SPoF. Furthermore, the line variant lacks
cyclic connections, limiting each client to a single iteration and preventing
the system from fully converging. In particular, in the line variant, the
clients at the front of the queue will have worse model performance. Given its
prominent disadvantages and advantages, it can serve as a baseline or initial
implementation for further research and development.

###  IV-C Variant: Ring

The ring variant corresponds to the cycle pointing line DFL, as depicted in
Fig. 4(b) and Fig. 6(a). The cyclic form is commonly used in DFL as the model
needs to be trained between clients to acquire new knowledge collected from
other clients, thereby enhancing generalization. Based on the framework, not
all past models need to be transferred for aggregation in each communication
since many of them may already be outdated. The ring variant not only inherits
the simplicity of the line variant but also becomes a popular approach in
various research papers due to its ability to iterate indefinitely until
convergence.

The ring topology is already considered mature in decentralized learning [209,
145] and is beginning to gain traction in DFL [210]. For example, Chang et al.
[163] proposed two heuristics for DFL, including sequential pointing
communication on each client for one iteration and multiple iterations to
obtain the final model. Similarly, Sheller et al. [165] also considered
sequential pointing or cycle continual learning in the client to generate the
final model. Nguyen et al. [181] applied cycle pointing DFL to autonomous
driving applications. Yuan et al. [167] proposed a random ring topology DFL
framework, named FedPC, based on the gossip communication protocol for
naturalistic driving action recognition. FedPC emphasizes the highly dynamic,
random, and data-heterogeneous nature of vehicle connections in this context.

###  IV-D Variant: Mesh

A multidirectional ring, also known as a fully connected topology, or be
called mesh, is a variant of the ring basic variant, depicted in Fig. 4(c) and
Fig. 6(b). In the ring variant, each client needs to transmit multiple model
parameters in each communication round, which can pose a burden on the network
bandwidth. In contrast, the mesh variant requires each client to transmit its
local model parameters to all other clients in each communication round. This
approach entails higher communication frequency for the clients while also
reducing the size of model packets transmitted per communication. The higher
communication frequency and larger per-communication data packet overhead have
their respective advantages and disadvantages, which can be traded off
depending on the specific application context. However, when compared to the
ring variant, the mesh variant significantly mitigates the impact of SPoF,
which is a notable advantage of this variant.

Recent research has witnessed the emergence of mesh-based DFL approaches [61,
211, 180, 172]. Assran et al. [168] proposed Stochastic Gradient Push (SGP), a
parallel broadcast-gossip mesh DFL approach. In the broadcast-gossip
iteration, clients in SGP send their trained local models to a sparse
selection of other clients in a parallel manner, and they also receive models
from other selected clients. Each client then performs a weighted aggregation
of its local model with the received models. Roy et al. [169] introduced the
BrainTorrent framework, in which a requesting client communicates with all
clients to obtain information about available model versions, and clients with
new versions send their models to the requesting client for aggregation.

###  IV-E Variant: Star

The star variant resembles the CFL model, where one client assumes the role of
the server to coordinate and interact with other clients, as depicted in Fig.
4(d) and Fig. 6(c). The star variant operates in two different modes. In the
first mode, similar to CFL, the central client is responsible for receiving,
aggregating, and distributing the local models. However, unlike CFL, the
central client also generates original data and utilizes the models for
perception and decision-making. This mode emphasizes a family-like
relationship, where one member has more computational and communication power
to assist the other clients. In the second mode of operation, the focus is on
geographic interoperability among clients. As some clients in the community
are geographically dispersed, there is a client that serves as the
geographical center for these clients. To conserve communication resources,
the surrounding clients transmit their models to the central client, which
then forwards the models to the other clients.

Pappas et al. [170] introduced a split learning framework within a star DFL
architecture, where clients train different layers of a model and update the
model parameters with the central client. This approach allows for distributed
model training and collaboration among clients. Another example of a star
variant is the Swarm Learning framework proposed by Warnat-Herresthal et al.
[53], which involves the dynamic election of a leader to aggregate model
parameters. In Swarm Learning, the leader plays a central role in coordinating
the aggregation process and facilitating collaboration among the clients.

###  IV-F Variant: Hybrid

The Hybrid variant of DFL encompasses a wide range of configurations,
combining elements from various other variants. It is considered the most
promising option for practical applications due to its adaptability to
different scenarios [212, 213, 214]. However, the complexity of configuring a
hybrid variant can pose challenges. One example of a hybrid variant, as
depicted in Fig. 4(g), involves connecting two ring variants through a central
client. In this configuration, the hybrid variant provides global
connectivity, allowing for the sharing of client models and knowledge within
the framework. The two ring variants can also be treated as a single entity,
with only one communication channel connected to the two central clients.
Another illustration of a hybrid variant, shown in Fig. 6(d), involves
dividing clients into organizations based on geographical locations (e.g.,
continents). Within each organization, a mesh topology network is established,
and a leader is elected. These leaders then form a ring topology network among
themselves. The hybrid variants do not have a fixed structure and can be
customized to meet the specific requirements of real-world scenarios. The
hybrid variant offers several advantages.

Firstly, the hybrid variant helps in saving communication resources. This is
achieved through the knowledge dissemination between the leaders of two
organizations, where only the aggregated global model is shared. By
transmitting only the essential information, the hybrid variant reduces the
communication overhead. Additionally, organized knowledge dissemination
further enhances resource efficiency by minimizing the sharing of irrelevant
or invalid information. This approach is particularly advantageous when
establishing communication between two geographically distant organizations,
as the single-line connection reduces the resource requirements for long-
distance communication. Considering that the communication between
organizations represents the dissemination of knowledge across states,
countries, and continents [215], the clients representing the institutions
establish a stable and well-structured communication connection to facilitate
the exchange of knowledge between their respective organizations.

Secondly, the hybrid variant offers enhanced security. With two central
clients in control, they have the ability to unilaterally disconnect the
communication between organizations, ensuring the protection of their
respective knowledge from potential leaks or unauthorized access. This adds an
extra layer of security to the DFL system.

Thirdly, the hybrid variant provides a more personalized approach. Each
organization’s aggregated model is organization-specific, tailored to the
unique characteristics of its local data. This personalized model may offer
better applicability to the specific needs and requirements of the
organization. While model knowledge is shared between the two organizations,
the decision of whether to utilize the other organization’s model is subject
to further investigation and discussion. By thoroughly assessing the
performance of the other organization’s model, clients can ensure that their
own model remains uncontaminated and unaffected by potentially inferior or
incompatible models.

Xing et al. [216] proposed a hybrid DFL network that establishes connections
only with neighboring clients, and model parameters are broadcast-gossiped
only among these neighboring clients. Their approach takes into account
various factors such as link blockages, channel fading, and mutual
interference, to ensure efficient and reliable communication. Building upon
this work, Shi et al. [171] further improved the convergence performance by
incorporating coding strategies, gradient tracking, and variance reduction
algorithms. In a similar vein, Wang et al. [173] developed a dynamic hybrid
DFL framework called Matcha. Matcha introduces the concept of creating
different network topologies at each iteration to enhance convergence speed.
The algorithm consists of two main parts. Firstly, an initial network topology
pre-processing step where Matcha performs matching decomposition on a base
communication topology to obtain disjoint sub-graphs, including sub-graphs
with only two-peer connections. Next, matching activation probabilities are
computed to maximize the connectivity of the graph, and a new random topology
graph is generated for each iteration. The key idea behind Matcha is to
achieve faster convergence by enabling more frequent communication on
connectivity-critical links (e.g., central clients) and reducing communication
latency by decreasing the frequency of communication on other connections.
Matcha is particularly advantageous for hybrid networks with unknown or
dynamic central clients. However, it may not exhibit the same advantages in
scenarios involving research institutions where central clients are known and
pre-determined.

##  V Challenge and Potential Solutions in DFL

Based on the current SOTA technology, this section aims to discuss and analyze
potential challenges and future research directions for DFL. Additionally, the
variants mentioned in Section IV can be regarded as potential solutions to
address these challenges.

###  V-A High Communication Overhead

DFL is widely recognized as an extremely communication resource-efficient
approach compared to CFL. However, researchers are still striving for further
savings in communication resources and reduced communication complexity [217].
We discuss the existing CFL frameworks in Section II-B, and we consider
introducing viable methods to achieve efficient communication in DFL. Wang et
al. [218] introduced a method called optimization of topology construction and
model compression (CoCo) that aims to improve communication efficiency and
convergence speed in DFL. CoCo achieves this by employing adaptive techniques
for constructing the DFL network topology and assigning an appropriate model
compression ratio to each participating client. It achieves this by adaptively
constructing the DFL network topology and assigning an appropriate model
compression ratio to each client.

In addition to model compression, it is also important to investigate how to
leverage efficient communication lines and reduce the overall communication
length. Variants such as star and hybrid variants, which select geocentric
clients and resource-rich clients as leaders, have been proven to be effective
solutions in this regard. Some researchers have also focused on addressing the
bandwidth differences among different communication lines [219, 220]. It is
worth noting that the dynamic hybrid variant proposed by Wang et al. [173]
emphasizes the importance of communication efficiency and suggests frequent
communication with key clients to achieve faster convergence. A considerable
body of research emphasizes the importance of efficient communication in DFL
and proposes various strategies and methods to reduce complexity and optimize
communication resources. Further exploration in this direction is expected to
facilitate the potential deployment of DFL frameworks in real-world
applications.

###  V-B Computational and Storage Burden

Compared to the CFL and Continual paradigm, the Aggregate paradigm imposes
significantly higher demands on client-side computational and storage
resources. As there is no dedicated server in the Aggregate paradigm, clients
are responsible for storing previous model parameters and performing
aggregation computations alongside local model training. Consequently, the
computational and storage burdens pose challenges for client hardware.

One potential solution is to adopt the transfer learning concept and fix the
weights of the lower layers in all models. In this approach, the lower layers
serve as feature extractors for a specific task and are expected to be similar
across models, while the higher-level representations remain task-specific. By
fixing these parameters, there is no need for gradient descent, aggregation
computations, or communication-related to these layers. Moreover, this
approach reduces storage requirements, thereby substantially mitigating the
resource consumption of the client. Currently, with the widespread
availability of high-performance GPU computing resources, the challenges
related to computational and storage burdens are gradually diminishing. This
is especially true in DFL scenarios where institutions and organizations serve
as clients. However, in contexts such as mobile services dominated by
smartphones and on-board units in vehicular edge devices, there is still value
in researching ways to reduce computational complexity and optimize storage
efficiency.

###  V-C Vulnerability in Cybersecurity

Network security has always been a major challenge in FL, and this challenge
is particularly prominent in DFL [221, 222, 223, 224]. In the traditional CFL
setting, clients communicate with a central server, typically operated by a
research institution or a large commercial organization. While there is still
potential for attacks and data poisoning between clients and the server,
communication is generally more regulated and protected compared to DFL. In
DFL, the knowledge exchange occurs directly among users within a local area
network, with free and unrestricted sharing agreements, which poses an
increased risk of privacy exposure. Malicious attacks from clients, poisoned
data, free-riding attacks, and other malicious behaviors are all possible in
this decentralized setting [225, 226].

Kuo et al. [227] proposed the integration of blockchain into a decentralized
learning framework to enhance privacy protection, which can also be applied to
DFL. Chen et al. [228] integrated a differential privacy mechanism based on
blockchain technology. Bellet et al. [149] introduced an asynchronous and
differential privacy algorithm in DFL to safeguard user privacy. He et al.
[50] addressed trust issues between clients by employing an online push-sum
algorithm to actively push local models to trusted clients. Shayan et al.
[229] proposed the Biscotti DFL system, which incorporates multiple privacy
and security protection techniques, including the Multi-Krum defense to
prevent poisoning attacks, differential privacy noise to protect privacy, and
secure aggregation. The future research direction in cybersecurity will
involve the roles of attackers and defenders, focusing on developing targeted
attack and defense mechanisms for different DFL variants.

###  V-D Lack of Incentive Mechanism

In the absence of server management, the issue of fairness in aggregation has
been effectively addressed in DFL. However, the lack of incentives and mutual
distrust among clients can significantly impact their willingness to
contribute knowledge. A key issue is the lack of incentives, which may lead to
free-riding attacks where clients choose to benefit from the models without
contributing their own knowledge [68, 230].

In the context of DFL, the feasibility of incentive mechanisms based on game
theory, such as Stackelberg games [231], raises questions due to the
requirements on game leaders, participants, and rewards. One potential
solution could be the integration of reputation-based incentive mechanisms
using blockchain and smart contracts. Kang et al. [105] proposed assigning
reputation scores to clients to represent and quantify their reliability.
Clients with higher contributions and reputations can receive greater rewards.
However, designing effective and practical incentive mechanisms for DFL
remains an open problem.

In cases where task providers do not exist or there are no explicit rewards,
punitive incentives may also be a potential solution. Clients who fail to
contribute or engage in malicious behavior could face penalties or reduced
access to the benefits of the DFL framework. Further research is needed to
explore and develop robust incentive mechanisms tailored specifically for DFL
systems. Designing effective incentive mechanisms to encourage active
participation, foster trust, and stimulate enthusiastic knowledge sharing will
greatly facilitate the dissemination of knowledge in DFL.

###  V-E Lack of Management

In DFL, the absence of a central server for managing all clients poses a
significant challenge in receiving and sharing knowledge in an organized
manner. The lack of central management can lead to confusion, particularly
among clients with varying sample sizes, computational resources, and
communication capabilities. In the ring variant, a client only needs to wait
for the model parameters from the previous client, while in the mesh variant,
a client needs to wait for model parameters from all other clients. Such
dependencies on other clients for model transmission can result in deadlocks,
causing the entire system to halt. Moreover, the communication among clients
may not be robust, considering the possibility of SPoF. The absence of
management is particularly problematic in the hybrid variant depicted in Fig.
6(d), where clients communicate globally. The lack of management can lead to
reduced operational efficiency, confusion regarding model versions, and
performance degradation.

To address the challenge of lack of management in DFL, researchers have
proposed several approaches. One approach is to pre-request the status of
other clients, such as their model versions, before initiating knowledge
transfer [169]. By obtaining accurate information, a client can then request
the transfer of the entire model data. Some star variants enforce the
knowledge dissemination flow among clients by designating a leader [178]. This
leader is responsible for regulating knowledge dissemination among the
remaining clients. Additionally, Chen et al. [179] introduced the BDFL
framework, a mesh DFL framework specifically designed for autonomous vehicles.
In this framework, a leader is randomly selected in each communication round,
offering advantages such as increased privacy and security protection against
Byzantine faults, as well as enhanced management through the leader’s command
issuance. In real-world scenarios, clients may face challenges where they lack
knowledge about each other’s statuses, leading to issues such as model version
discrepancies and even system paralysis, such as in the case of an SPoF.
Therefore, future research directions aim to ensure the smooth operation of
the system by incorporating additional information or establishing contingency
plans. These measures can help mitigate the impact of uncertainty and improve
the reliability and robustness of the DFL framework.

##  VI Conclusion

In this paper, we provided an extensive exploration of the DFL framework,
covering communication protocols, network topologies, paradigm proposals,
extension variants, challenges, and potential solutions. Our aim is to offer a
comprehensive, well-defined, and systematic perspective that organizes and
synthesizes the existing literature and definitions, thereby facilitating a
comprehensive introduction to DFL for new researchers. Given that DFL is a
rapidly evolving area, we established a solid theoretical foundation by
defining and discussing five variants in this paper. This not only provides
researchers with a comprehensive understanding of the field but also fosters
the generation of new ideas and collaborations among peers.

It is important to note that our approach differs from traditional surveys, as
we presented our own insights and innovative thinking on DFL. Moreover, this
paper uncovers a considerable number of previously unexplored types within the
DFL framework. For example, no existing studies have demonstrated the
integration of the Continual paradigm with mesh network topology. Researchers
might consider asynchronous DFL, where clients acquire data and learn at
different times, thus benefiting from the dual advantages offered by the
Continual paradigm and the mesh network topology, such as reduced
communication overhead, personalization, and more. By considering diverse
usage scenarios, we aim to stimulate and extend the research interest of other
DFL practitioners, enabling them to adapt the framework to their specific
needs.

## References

  * [1] P. Kairouz, H. B. McMahan, B. Avent, A. Bellet, M. Bennis, A. N. Bhagoji, K. Bonawitz, Z. Charles, G. Cormode, R. Cummings _et al._ , “Advances and open problems in federated learning,” _Found. Trends Mach. Learn._ , vol. 14, no. 1–2, pp. 1–210, Jun. 2021.
  * [2] B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” in _Artificial intelligence and statistics_. PMLR, 2017, pp. 1273–1282.
  * [3] A. Hard, K. Rao, R. Mathews, S. Ramaswamy, F. Beaufays, S. Augenstein, H. Eichner, C. Kiddon, and D. Ramage, “Federated learning for mobile keyboard prediction,” _arXiv preprint arXiv:1811.03604_ , 2018.
  * [4] D. Ramage and S. Mazzocchi, “Federated analytics: Collaborative data science without data collection,” _Google Research_ , 2020.
  * [5] D. Wang, S. Shi, Y. Zhu, and Z. Han, “Federated analytics: Opportunities and challenges,” _IEEE Network_ , vol. 36, no. 1, pp. 151–158, 2021.
  * [6] A. R. Elkordy, Y. H. Ezzeldin, S. Han, S. Sharma, C. He, S. Mehrotra, S. Avestimehr _et al._ , “Federated analytics: A survey,” _APSIPA Transactions on Signal and Information Processing_ , vol. 12, no. 1, 2023.
  * [7] D. Chen, D. Wang, Y. Zhu, and Z. Han, “Digital twin for federated analytics using a bayesian approach,” _IEEE Internet of Things Journal_ , vol. 8, no. 22, pp. 16 301–16 312, 2021.
  * [8] S. R. Pandey, M. N. Nguyen, T. N. Dang, N. H. Tran, K. Thar, Z. Han, and C. S. Hong, “Edge-assisted democratized learning toward federated analytics,” _IEEE Internet of Things Journal_ , vol. 9, no. 1, pp. 572–588, 2021.
  * [9] Z. Wang, Y. Zhu, D. Wang, and Z. Han, “FedFPM: A unified federated analytics framework for collaborative frequent pattern mining,” in _IEEE INFOCOM 2022-IEEE Conference on Computer Communications_. IEEE, 2022, pp. 61–70.
  * [10] D. Froelicher, J. R. Troncoso-Pastoriza, J. L. Raisaro, M. A. Cuendet, J. S. Sousa, H. Cho, B. Berger, J. Fellay, and J.-P. Hubaux, “Truly privacy-preserving federated analytics for precision medicine with multiparty homomorphic encryption,” _Nature communications_ , vol. 12, no. 1, p. 5910, 2021.
  * [11] Z. Wang, R. Gupta, K. Han, H. Wang, A. Ganlath, N. Ammar, and P. Tiwari, “Mobility digital twin: Concept, architecture, case study, and future challenges,” _IEEE Internet Things J._ , Mar. 2022.
  * [12] L. U. Khan, W. Saad, Z. Han, E. Hossain, and C. S. Hong, “Federated learning for internet of things: Recent advances, taxonomy, and open challenges,” _IEEE Communications Surveys & Tutorials_, vol. 23, no. 3, pp. 1759–1799, 2021.
  * [13] Y. Li, B. Dang, W. Li, and Y. Zhang, “Glh-water: A large-scale dataset for global surface water detection in large-size very-high-resolution satellite imagery,” in _Proceedings of the AAAI Conference on Artificial Intelligence_ , vol. 38, no. 20, 2024, pp. 22 213–22 221.
  * [14] Y. Li, J. Luo, Y. Zhang, Y. Tan, J.-G. Yu, and S. Bai, “Learning to holistically detect bridges from large-size vhr remote sensing imagery,” _IEEE Transactions on Pattern Analysis and Machine Intelligence_ , 2024.
  * [15] G. A. Kaissis, M. R. Makowski, D. Rückert, and R. F. Braren, “Secure, privacy-preserving and federated machine learning in medical imaging,” _Nat. Mach. Intell._ , vol. 2, no. 6, pp. 305–311, Jun. 2020.
  * [16] A. Durrant, M. Markovic, D. Matthews, D. May, J. Enright, and G. Leontidis, “The role of cross-silo federated learning in facilitating data sharing in the agri-food sector,” _Comput. Electron. Agric._ , vol. 193, p. 106648, Feb. 2022.
  * [17] Y. M. Saputra, D. T. Hoang, D. N. Nguyen, E. Dutkiewicz, M. D. Mueck, and S. Srikanteswara, “Energy demand prediction with federated learning for electric vehicle networks,” in _2019 IEEE Global Communications Conference (GLOBECOM)_. IEEE, 2019, pp. 1–6.
  * [18] L. Yuan, D.-J. Han, V. P. Chellapandi, S. H. Żak, and C. G. Brinton, “FedMFS: Federated multimodal fusion learning with selective modality communication,” _arXiv preprint arXiv:2310.07048_ , 2023.
  * [19] D. Shi, L. Li, R. Chen, P. Prakash, M. Pan, and Y. Fang, “Toward energy-efficient federated learning over 5g+ mobile devices,” _IEEE Wireless Communications_ , vol. 29, no. 5, pp. 44–51, 2022.
  * [20] B. Pfitzner, N. Steckhan, and B. Arnrich, “Federated learning in a medical context: a systematic literature review,” _ACM Transactions on Internet Technology (TOIT)_ , vol. 21, no. 2, pp. 1–31, 2021.
  * [21] D. C. Nguyen, Q.-V. Pham, P. N. Pathirana, M. Ding, A. Seneviratne, Z. Lin, O. Dobre, and W.-J. Hwang, “Federated learning for smart healthcare: A survey,” _ACM Comput. Surv. (CSUR)_ , vol. 55, no. 3, pp. 1–37, 2022.
  * [22] J. Li, Y. Meng, L. Ma, S. Du, H. Zhu, Q. Pei, and X. Shen, “A federated learning based privacy-preserving smart healthcare system,” _IEEE Transactions on Industrial Informatics_ , vol. 18, no. 3, 2021.
  * [23] J. C. Jiang, B. Kantarci, S. Oktug, and T. Soyata, “Federated learning in smart city sensing: Challenges and opportunities,” _Sensors_ , vol. 20, no. 21, p. 6230, 2020.
  * [24] Z. Yang, M. Chen, K.-K. Wong, H. V. Poor, and S. Cui, “Federated learning for 6g: Applications, challenges, and opportunities,” _Engineering_ , vol. 8, pp. 33–41, 2022.
  * [25] V. P. Chellapandi, Y. Nagaraj, J. Supplee, S. Hernandez-Gonzalez, H. Borhan, and S. H. Żak, “Predictive control of diesel oxidation catalysts with federated learning in connected vehicles,” in _2024 Forum for Innovative Sustainable Transportation Systems (FISTS)_. IEEE, 2024, pp. 1–6.
  * [26] L. Sun and J. Wu, “A scalable and transferable federated learning system for classifying healthcare sensor data,” _IEEE Journal of Biomedical and Health Informatics_ , vol. 27, no. 2, pp. 866–877, 2022.
  * [27] Y.-W. Chu, D.-J. Han, and C. G. Brinton, “Only send what you need: Learning to communicate efficiently in federated multilingual machine translation,” _arXiv preprint arXiv:2401.07456_ , 2024.
  * [28] L. Yuan, D.-J. Han, S. Wang, D. Upadhyay, and C. G. Brinton, “Communication-efficient multimodal federated learning: Joint modality and client selection,” _arXiv preprint arXiv:2401.16685_ , 2024.
  * [29] J. Tan, Y. Li, S. A. Bartalev, B. Dang, W. Chen, Y. Zhang, and L. Yuan, “Bridging data islands: Geographic heterogeneity-aware federated learning for collaborative remote sensing semantic segmentation,” _arXiv preprint arXiv:2404.09292_ , 2024.
  * [30] I. Dayan, H. R. Roth, A. Zhong, A. Harouni, A. Gentili, A. Z. Abidin, A. Liu, A. B. Costa, B. J. Wood, C.-S. Tsai _et al._ , “Federated learning for predicting clinical outcomes in patients with covid-19,” _Nat. Med._ , vol. 27, no. 10, pp. 1735–1743, Oct. 2021.
  * [31] S. Pati, U. Baid, B. Edwards, M. Sheller, S.-H. Wang, G. A. Reina, P. Foley, A. Gruzdev, D. Karkada, C. Davatzikos _et al._ , “Federated learning enables big data for rare cancer boundary detection,” _Nat. Commun._ , vol. 13, no. 1, p. 7346, Dec. 2022.
  * [32] Q. Yang, Y. Liu, T. Chen, and Y. Tong, “Federated machine learning: Concept and applications,” _ACM Trans. Intell. Syst. (TIST)_ , vol. 10, no. 2, pp. 1–19, Jan. 2019.
  * [33] H. Yu, Z. Liu, Y. Liu, T. Chen, M. Cong, X. Weng, D. Niyato, and Q. Yang, “A fairness-aware incentive scheme for federated learning,” in _Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society_ , 2020, pp. 393–399.
  * [34] M. Chen, N. Shlezinger, H. V. Poor, Y. C. Eldar, and S. Cui, “Communication-efficient federated learning,” _Proceedings of the National Academy of Sciences_ , vol. 118, no. 17, p. e2024789118, 2021.
  * [35] Z. Yang, Y. Shi, Y. Zhou, Z. Wang, and K. Yang, “Trustworthy federated learning via blockchain,” _IEEE Internet of Things Journal_ , vol. 10, no. 1, pp. 92–109, 2022.
  * [36] M. Zhang, E. Wei, and R. Berry, “Faithful edge federated learning: Scalability and privacy,” _IEEE Journal on Selected Areas in Communications_ , vol. 39, no. 12, pp. 3790–3804, 2021.
  * [37] K. Bonawitz, H. Eichner, W. Grieskamp, D. Huba, A. Ingerman, V. Ivanov, C. Kiddon, J. Konečnỳ, S. Mazzocchi, B. McMahan _et al._ , “Towards federated learning at scale: System design,” _Proceedings of machine learning and systems_ , vol. 1, pp. 374–388, 2019.
  * [38] Q. Li, Z. Wen, Z. Wu, S. Hu, N. Wang, Y. Li, X. Liu, and B. He, “A survey on federated learning systems: Vision, hype and reality for data privacy and protection,” _IEEE Transactions on Knowledge and Data Engineering_ , 2021.
  * [39] F. Lai, Y. Dai, S. Singapuram, J. Liu, X. Zhu, H. Madhyastha, and M. Chowdhury, “Fedscale: Benchmarking model and system performance of federated learning at scale,” in _International Conference on Machine Learning_. PMLR, 2022, pp. 11 814–11 827.
  * [40] S. Agrawal, S. Sarkar, O. Aouedi, G. Yenduri, K. Piamrat, M. Alazab, S. Bhattacharya, P. K. R. Maddikunta, and T. R. Gadekallu, “Federated learning for intrusion detection system: Concepts, challenges and future directions,” _Computer Communications_ , 2022.
  * [41] R. S. Antunes, C. André da Costa, A. Küderle, I. A. Yari, and B. Eskofier, “Federated learning for healthcare: Systematic review and architecture proposal,” _ACM Transactions on Intelligent Systems and Technology (TIST)_ , vol. 13, no. 4, pp. 1–23, 2022.
  * [42] J. Kang, Z. Xiong, D. Niyato, Y. Zou, Y. Zhang, and M. Guizani, “Reliable federated learning for mobile networks,” _IEEE Wireless Communications_ , vol. 27, no. 2, pp. 72–80, 2020.
  * [43] L. Lyu, H. Yu, and Q. Yang, “Threats to federated learning: A survey,” _arXiv preprint arXiv:2003.02133_ , 2020.
  * [44] Y. Zhan, P. Li, Z. Qu, D. Zeng, and S. Guo, “A learning-based incentive mechanism for federated learning,” _IEEE Internet of Things Journal_ , vol. 7, no. 7, pp. 6360–6368, 2020.
  * [45] A. Blanco-Justicia, J. Domingo-Ferrer, S. Martínez, D. Sánchez, A. Flanagan, and K. E. Tan, “Achieving security and privacy in federated learning systems: Survey, research challenges and future directions,” _Engineering Applications of Artificial Intelligence_ , vol. 106, p. 104468, 2021.
  * [46] H. Yu, Z. Liu, Y. Liu, T. Chen, M. Cong, X. Weng, D. Niyato, and Q. Yang, “A sustainable incentive scheme for federated learning,” _IEEE Intelligent Systems_ , vol. 35, no. 4, pp. 58–69, 2020.
  * [47] S. Wan, J. Lu, P. Fan, Y. Shao, C. Peng, and K. B. Letaief, “Convergence analysis and system design for federated learning over wireless networks,” _IEEE Journal on Selected Areas in Communications_ , vol. 39, no. 12, pp. 3622–3639, 2021.
  * [48] A. Ghosh, J. Hong, D. Yin, and K. Ramchandran, “Robust federated learning in a heterogeneous environment,” _arXiv preprint arXiv:1906.06629_ , 2019.
  * [49] A. Lalitha, O. C. Kilinc, T. Javidi, and F. Koushanfar, “Peer-to-peer federated learning on graphs,” _arXiv preprint arXiv:1901.11173_ , 2019.
  * [50] C. He, C. Tan, H. Tang, S. Qiu, and J. Liu, “Central server free federated learning over single-sided trust social networks,” _arXiv preprint arXiv:1910.04956_ , 2019.
  * [51] C. He, E. Ceyani, K. Balasubramanian, M. Annavaram, and S. Avestimehr, “Spreadgnn: Serverless multi-task federated learning for graph neural networks,” _arXiv preprint arXiv:2106.02743_ , 2021.
  * [52] H. Xing, O. Simeone, and S. Bi, “Federated learning over wireless device-to-device networks: Algorithms and convergence analysis,” _IEEE J. Sel. Areas Commun._ , vol. 39, no. 12, pp. 3723–3741, 2021.
  * [53] S. Warnat-Herresthal, H. Schultze, K. L. Shastry, S. Manamohan, S. Mukherjee, V. Garg, R. Sarveswara, K. Händler, P. Pickkers, N. A. Aziz _et al._ , “Swarm learning for decentralized and confidential clinical machine learning,” _Nature_ , vol. 594, no. 7862, pp. 265–270, Jun. 2021.
  * [54] A. Lalitha, S. Shekhar, T. Javidi, and F. Koushanfar, “Fully decentralized federated learning,” in _Third workshop on Bayesian Deep Learning (NeurIPS)_ , 2018.
  * [55] Q. Li, Z. Wen, and B. He, “Practical federated gradient boosting decision trees,” in _Proceedings of the AAAI conference on artificial intelligence_ , vol. 34, no. 04, 2020, pp. 4642–4649.
  * [56] S. Savazzi, M. Nicoli, and V. Rampa, “Federated learning with cooperating devices: A consensus approach for massive IoT networks,” _IEEE Internet of Things Journal_ , vol. 7, no. 5, pp. 4641–4654, 2020.
  * [57] D. Monschein, J. A. P. Pérez, T. Piotrowski, Z. Nochta, O. P. Waldhorst, and C. Zirpins, “Towards a peer-to-peer federated machine learning environment for continuous authentication,” in _2021 IEEE Symposium on Computers and Communications (ISCC)_. IEEE, 2021, pp. 1–6.
  * [58] S. Savazzi, M. Nicoli, M. Bennis, S. Kianoush, and L. Barbieri, “Opportunities of federated learning in connected, cooperative, and automated industrial systems,” _IEEE Commun. Mag._ , vol. 59, no. 2, pp. 16–21, Mar. 2021.
  * [59] Y. Shi, L. Shen, K. Wei, Y. Sun, B. Yuan, X. Wang, and D. Tao, “Improving the model consistency of decentralized federated learning,” _arXiv preprint arXiv:2302.04083_ , Feb. 2023.
  * [60] V. P. Chellapandi, A. Upadhyay, A. Hashemi, and S. H. Żak, “On the convergence of decentralized federated learning under imperfect information sharing,” _IEEE Control Systems Letters_ , 2023.
  * [61] J. Xu, B. S. Glicksberg, C. Su, P. Walker, J. Bian, and F. Wang, “Federated learning for healthcare informatics,” _J. Healthc. Inform. Res._ , vol. 5, no. 1, pp. 1–19, Mar. 2021.
  * [62] X. Lian, C. Zhang, H. Zhang, C.-J. Hsieh, W. Zhang, and J. Liu, “Can decentralized algorithms outperform centralized algorithms? a case study for decentralized parallel stochastic gradient descent,” _Advances in Neural Information Processing Systems_ , vol. 30, 2017.
  * [63] X. Yin, Y. Zhu, and J. Hu, “A comprehensive survey of privacy-preserving federated learning: A taxonomy, review, and future directions,” _ACM Computing Surveys (CSUR)_ , vol. 54, no. 6, pp. 1–36, 2021.
  * [64] Y. Qu, H. Dai, Y. Zhuang, J. Chen, C. Dong, F. Wu, and S. Guo, “Decentralized federated learning for uav networks: Architecture, challenges, and opportunities,” _IEEE Network_ , vol. 35, no. 6, pp. 156–162, 2021.
  * [65] D. C. Nguyen, M. Ding, P. N. Pathirana, A. Seneviratne, J. Li, and H. V. Poor, “Federated learning for internet of things: A comprehensive survey,” _IEEE Communications Surveys & Tutorials_, vol. 23, no. 3, pp. 1622–1658, 2021.
  * [66] Y. Qu, M. P. Uddin, C. Gan, Y. Xiang, L. Gao, and J. Yearwood, “Blockchain-enabled federated learning: A survey,” _ACM Computing Surveys_ , vol. 55, no. 4, pp. 1–35, 2022.
  * [67] R. Gupta and T. Alam, “Survey on federated-learning approaches in distributed environment,” _Wireless personal communications_ , vol. 125, no. 2, pp. 1631–1652, 2022.
  * [68] L. Witt, M. Heyer, K. Toyoda, W. Samek, and D. Li, “Decentral and incentivized federated learning frameworks: A systematic literature review,” _IEEE Internet of Things Journal_ , vol. 10, no. 4, pp. 3642–3663, 2022.
  * [69] H. Gao, M. T. Thai, and J. Wu, “When decentralized optimization meets federated learning,” _IEEE network_ , 2023.
  * [70] E. Gabrielli, G. Pica, and G. Tolomei, “A survey on decentralized federated learning,” _arXiv preprint arXiv:2308.04604_ , 2023.
  * [71] J. Wu, F. Dong, H. Leung, Z. Zhu, J. Zhou, and S. Drew, “Topology-aware federated learning in edge computing: A comprehensive survey,” _ACM Computing Surveys_ , 2023.
  * [72] V. P. Chellapandi, L. Yuan, C. G. Brinton, S. H. Żak, and Z. Wang, “Federated learning for connected and automated vehicles: A survey of existing approaches and challenges,” _IEEE Trans. Intell. Veh._ , vol. 9, no. 1, pp. 119 – 137, November 2023.
  * [73] E. T. M. Beltrán, M. Q. Pérez, P. M. S. Sánchez, S. L. Bernal, G. Bovet, M. G. Pérez, G. M. Pérez, and A. H. Celdrán, “Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges,” _IEEE Communications Surveys & Tutorials_, 2023.
  * [74] E. Hallaji, R. Razavi-Far, M. Saif, B. Wang, and Q. Yang, “Decentralized federated learning: A survey on security and privacy,” _IEEE Transactions on Big Data_ , 2024.
  * [75] N. Rieke, J. Hancox, W. Li, F. Milletari, H. R. Roth, S. Albarqouni, S. Bakas, M. N. Galtier, B. A. Landman, K. Maier-Hein _et al._ , “The future of digital health with federated learning,” _NPJ Digit. Med._ , vol. 3, no. 1, pp. 1–7, Sep. 2020.
  * [76] V. Mothukuri, R. M. Parizi, S. Pouriyeh, Y. Huang, A. Dehghantanha, and G. Srivastava, “A survey on security and privacy of federated learning,” _Future Gener. Comput. Syst._ , vol. 115, pp. 619–640, Feb. 2021.
  * [77] V. P. Chellapandi, L. Yuan, S. H. Żak, and Z. Wang, “A survey of federated learning for connected and automated vehicles,” in _2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)_. IEEE, 2023, pp. 2485–2492.
  * [78] S. P. Karimireddy, S. Kale, M. Mohri, S. Reddi, S. Stich, and A. T. Suresh, “Scaffold: Stochastic controlled averaging for federated learning,” in _International Conference on Machine Learning_. PMLR, 2020, pp. 5132–5143.
  * [79] Y. Zhao, M. Li, L. Lai, N. Suda, D. Civin, and V. Chandra, “Federated learning with non-iid data,” _arXiv preprint arXiv:1806.00582_ , 2018.
  * [80] T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V. Smith, “Federated optimization in heterogeneous networks,” _Proceedings of Machine Learning and Systems_ , vol. 2, pp. 429–450, 2020.
  * [81] K. Wei, J. Li, M. Ding, C. Ma, H. H. Yang, F. Farokhi, S. Jin, T. Q. Quek, and H. V. Poor, “Federated learning with differential privacy: Algorithms and performance analysis,” _IEEE Trans. Inf. Forensics Secur._ , vol. 15, pp. 3454–3469, Apr. 2020.
  * [82] T. R. Gadekallu, Q.-V. Pham, T. Huynh-The, S. Bhattacharya, P. K. R. Maddikunta, and M. Liyanage, “Federated learning for big data: A survey on opportunities, applications, and future directions,” _arXiv preprint arXiv:2110.04160_ , 2021.
  * [83] A. Ghosh, J. Chung, D. Yin, and K. Ramchandran, “An efficient framework for clustered federated learning,” _Advances in Neural Information Processing Systems_ , vol. 33, pp. 19 586–19 597, 2020.
  * [84] G. Long, M. Xie, T. Shen, T. Zhou, X. Wang, and J. Jiang, “Multi-center federated learning: clients clustering for better personalization,” _World Wide Web_ , vol. 26, no. 1, pp. 481–500, 2023.
  * [85] M. Ye, X. Fang, B. Du, P. C. Yuen, and D. Tao, “Heterogeneous federated learning: State-of-the-art and research challenges,” _ACM Comput. Surv._ , vol. 56, no. 3, pp. 1–44, 2023.
  * [86] W. Fang, Y. Jiang, Y. Shi, Y. Zhou, W. Chen, and K. B. Letaief, “Over-the-air computation via reconfigurable intelligent surface,” _IEEE transactions on communications_ , vol. 69, no. 12, pp. 8612–8626, 2021.
  * [87] W. Fang, Z. Yu, Y. Jiang, Y. Shi, C. N. Jones, and Y. Zhou, “Communication-efficient stochastic zeroth-order optimization for federated learning,” _IEEE Transactions on Signal Processing_ , vol. 70, pp. 5058–5073, 2022.
  * [88] J. Konečnỳ, H. B. McMahan, F. X. Yu, P. Richtárik, A. T. Suresh, and D. Bacon, “Federated learning: Strategies for improving communication efficiency,” _arXiv preprint arXiv:1610.05492_ , 2016.
  * [89] F. Sattler, S. Wiedemann, K.-R. Müller, and W. Samek, “Robust and communication-efficient federated learning from non-iid data,” _IEEE Trans. Neural Netw. Learn. Syst._ , vol. 31, no. 9, pp. 3400–3413, Nov. 2019.
  * [90] G. Lan, X.-Y. Liu, Y. Zhang, and X. Wang, “Communication-efficient federated learning for resource-constrained edge devices,” _IEEE Transactions on Machine Learning in Communications and Networking_ , 2023.
  * [91] G. Lan, H. Wang, J. Anderson, C. Brinton, and V. Aggarwal, “Improved communication efficiency in federated natural policy gradient via admm-based gradient updates,” _arXiv preprint arXiv:2310.19807_ , 2023.
  * [92] A. Imteaj, U. Thakker, S. Wang, J. Li, and M. H. Amini, “A survey on federated learning for resource-constrained IoT devices,” _IEEE Internet of Things Journal_ , vol. 9, no. 1, pp. 1–24, 2021.
  * [93] S. Wang, T. Tuor, T. Salonidis, K. K. Leung, C. Makaya, T. He, and K. Chan, “Adaptive federated learning in resource constrained edge computing systems,” _IEEE journal on selected areas in communications_ , vol. 37, no. 6, pp. 1205–1221, 2019.
  * [94] A. Imteaj, K. Mamun Ahmed, U. Thakker, S. Wang, J. Li, and M. H. Amini, “Federated learning for resource-constrained IoT devices: Panoramas and state of the art,” _Federated and Transfer Learning_ , pp. 7–27, 2022.
  * [95] M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “Mobilenetv2: Inverted residuals and linear bottlenecks,” in _Proceedings of the IEEE conference on computer vision and pattern recognition_ , 2018, pp. 4510–4520.
  * [96] M. Tan and Q. Le, “Efficientnet: Rethinking model scaling for convolutional neural networks,” in _International conference on machine learning_. PMLR, 2019, pp. 6105–6114.
  * [97] F. Sattler, S. Wiedemann, K.-R. Müller, and W. Samek, “Sparse binary compression: Towards distributed deep learning with minimal communication,” in _2019 International Joint Conference on Neural Networks (IJCNN)_. IEEE, 2019, pp. 1–8.
  * [98] W. Zhang, D. Yang, H. Peng, W. Wu, W. Quan, H. Zhang, and X. Shen, “Deep reinforcement learning based resource management for DNN inference in industrial IoT,” _IEEE Transactions on Vehicular Technology_ , vol. 70, no. 8, pp. 7605–7618, 2021.
  * [99] C. Ma, J. Li, M. Ding, H. H. Yang, F. Shu, T. Q. Quek, and H. V. Poor, “On safeguarding privacy and security in the framework of federated learning,” _IEEE Netw._ , vol. 34, no. 4, pp. 242–248, Mar. 2020.
  * [100] E. Bagdasaryan, A. Veit, Y. Hua, D. Estrin, and V. Shmatikov, “How to backdoor federated learning,” in _International Conference on Artificial Intelligence and Statistics_. PMLR, 2020, pp. 2938–2948.
  * [101] V. Tolpegin, S. Truex, M. E. Gursoy, and L. Liu, “Data poisoning attacks against federated learning systems,” in _European Symposium on Research in Computer Security_. Springer, 2020, pp. 480–501.
  * [102] V. Mothukuri, P. Khare, R. M. Parizi, S. Pouriyeh, A. Dehghantanha, and G. Srivastava, “Federated-learning-based anomaly detection for IoT security attacks,” _IEEE Internet of Things Journal_ , vol. 9, no. 4, pp. 2545–2554, 2021.
  * [103] Y. Zhang, D. Zeng, J. Luo, Z. Xu, and I. King, “A survey of trustworthy federated learning with perspectives on security, robustness, and privacy,” _arXiv preprint arXiv:2302.10637_ , 2023.
  * [104] T. Li, M. Sanjabi, A. Beirami, and V. Smith, “Fair resource allocation in federated learning,” _arXiv preprint arXiv:1905.10497_ , 2019.
  * [105] J. Kang, Z. Xiong, D. Niyato, S. Xie, and J. Zhang, “Incentive mechanism for reliable federated learning: A joint optimization approach to combining reputation and contract theory,” _IEEE Internet Things J._ , vol. 6, no. 6, pp. 10 700–10 714, Sep. 2019.
  * [106] D. Roschewitz, M.-A. Hartley, L. Corinzia, and M. Jaggi, “IFedAvg: Interpretable data-interoperability for federated learning,” _arXiv preprint arXiv:2107.06580_ , 2021.
  * [107] M. Salehi and E. Hossain, “Federated learning in unreliable and resource-constrained cellular wireless networks,” _IEEE Transactions on Communications_ , vol. 69, no. 8, pp. 5136–5151, 2021.
  * [108] S. Ganguli, Z. Zhou, C. G. Brinton, and D. I. Inouye, “Fault-tolerant vertical federated learning on dynamic networks,” _arXiv preprint arXiv:2312.16638_ , 2023.
  * [109] S. Ganguli, A. Amalanshu, A. Ranjan, and D. I. Inouye, “Internet learning: Preliminary steps towards highly fault-tolerant learning on device networks,” in _ICML Workshop on Localized Learning (LLW)_ , 2023.
  * [110] H. Kim, J. Park, M. Bennis, and S.-L. Kim, “Blockchained on-device federated learning,” _IEEE Commun. Lett._ , vol. 24, no. 6, pp. 1279–1283, Jun. 2019.
  * [111] Y. Qu, L. Gao, T. H. Luan, Y. Xiang, S. Yu, B. Li, and G. Zheng, “Decentralized privacy using blockchain-enabled federated learning in fog computing,” _IEEE Internet Things J._ , vol. 7, no. 6, pp. 5171–5183, Mar. 2020.
  * [112] W. Zhang, D. Yang, W. Wu, H. Peng, H. Zhang, and X. S. Shen, “Spectrum and computing resource management for federated learning in distributed industrial IoT,” in _2021 IEEE International Conference on Communications Workshops (ICC Workshops)_. IEEE, 2021, pp. 1–6.
  * [113] W. Zhang, D. Yang, W. Wu, H. Peng, N. Zhang, H. Zhang, and X. Shen, “Optimizing federated learning in distributed industrial IoT: A multi-agent approach,” _IEEE Journal on Selected Areas in Communications_ , vol. 39, no. 12, pp. 3688–3703, 2021.
  * [114] D. Yang, W. Zhang, Q. Ye, C. Zhang, N. Zhang, C. Huang, H. Zhang, and X. Shen, “DetFed: Dynamic resource scheduling for deterministic federated learning over time-sensitive networks,” _IEEE Transactions on Mobile Computing_ , 2023.
  * [115] W. Y. B. Lim, N. C. Luong, D. T. Hoang, Y. Jiao, Y.-C. Liang, Q. Yang, D. Niyato, and C. Miao, “Federated learning in mobile edge networks: A comprehensive survey,” _IEEE Commun. Surv. Tutor._ , vol. 22, no. 3, pp. 2031–2063, Apr. 2020.
  * [116] Y. Ye, S. Li, F. Liu, Y. Tang, and W. Hu, “Edgefed: Optimized federated learning based on edge computing,” _IEEE Access_ , vol. 8, pp. 209 191–209 198, Nov. 2020.
  * [117] S. Wang, S. Hosseinalipour, V. Aggarwal, C. G. Brinton, D. J. Love, W. Su, and M. Chiang, “Towards cooperative federated learning over heterogeneous edge/fog networks,” _IEEE Communications Magazine_ , 2023.
  * [118] W. Fang, D.-J. Han, and C. G. Brinton, “Submodel partitioning in hierarchical federated learning: Algorithm design and convergence analysis,” _arXiv preprint arXiv:2310.17890_ , 2023.
  * [119] E. Chen, S. Wang, and C. G. Brinton, “Taming subnet-drift in D2D-enabled fog learning: A hierarchical gradient tracking approach,” _arXiv preprint arXiv:2312.04728_ , 2023.
  * [120] Y.-W. Chu, S. Hosseinalipour, E. Tenorio, L. Cruz, K. Douglas, A. Lan, and C. Brinton, “Mitigating biases in student performance prediction via attention-based personalized federated learning,” in _Proceedings of the 31st ACM International Conference on Information & Knowledge Management_, 2022, pp. 3033–3042.
  * [121] A. Z. Tan, H. Yu, L. Cui, and Q. Yang, “Towards personalized federated learning,” _IEEE Trans. Neural Netw. Learn. Syst._ , Mar. 2022.
  * [122] Y.-W. Chu, S. Hosseinalipour, E. Tenorio, L. Cruz, K. Douglas, A. Lan, and C. Brinton, “Multi-layer personalized federated learning for mitigating biases in student predictive analytics,” _arXiv preprint arXiv:2212.02985_ , 2022.
  * [123] Y. Chen, X. Qin, J. Wang, C. Yu, and W. Gao, “Fedhealth: A federated transfer learning framework for wearable healthcare,” _IEEE Intell. Syst._ , vol. 35, no. 4, pp. 83–93, Apr. 2020.
  * [124] L. Yuan, L. Su, and Z. Wang, “Federated transfer-ordered-personalized learning for driver monitoring application,” _IEEE Internet of Things Journal_ , vol. 10, no. 20, pp. 18 292 – 18 301, May 2023.
  * [125] F. Sattler, K.-R. Müller, and W. Samek, “Clustered federated learning: Model-agnostic distributed multitask optimization under privacy constraints,” _IEEE Trans. Neural Netw. Learn. Syst._ , vol. 32, no. 8, pp. 3710–3722, Aug. 2020.
  * [126] C. Thapa, P. C. M. Arachchige, S. Camtepe, and L. Sun, “Splitfed: When federated learning meets split learning,” in _Proceedings of the AAAI Conference on Artificial Intelligence_ , vol. 36, no. 8, 2022, pp. 8485–8493.
  * [127] D.-J. Han, D.-Y. Kim, M. Choi, C. G. Brinton, and J. Moon, “Splitgp: Achieving both generalization and personalization in federated learning,” in _IEEE INFOCOM 2023-IEEE Conference on Computer Communications_. IEEE, 2023, pp. 1–10.
  * [128] D.-J. Han, D.-Y. Kim, M. Choi, D. Nickel, J. Moon, M. Chiang, and C. G. Brinton, “Federated split learning with joint personalization-generalization for inference-stage optimization in wireless edge networks,” _IEEE Transactions on Mobile Computing_ , 2023.
  * [129] L. Qu, N. Tang, R. Zheng, Q. V. H. Nguyen, Z. Huang, Y. Shi, and H. Yin, “Semi-decentralized federated ego graph learning for recommendation,” in _Proceedings of the ACM Web Conference 2023_ , 2023, pp. 339–348.
  * [130] C. He, K. Balasubramanian, E. Ceyani, C. Yang, H. Xie, L. Sun, L. He, L. Yang, P. S. Yu, Y. Rong _et al._ , “FedGraphNN: A federated learning system and benchmark for graph neural networks,” _arXiv preprint arXiv:2104.07145_ , 2021.
  * [131] J. Baek, W. Jeong, J. Jin, J. Yoon, and S. J. Hwang, “Personalized subgraph federated learning,” in _International Conference on Machine Learning_. PMLR, 2023, pp. 1396–1415.
  * [132] R. Liu, P. Xing, Z. Deng, A. Li, C. Guan, and H. Yu, “Federated graph neural networks: Overview, techniques, and challenges,” _IEEE Transactions on Neural Networks and Learning Systems_ , 2024.
  * [133] S. Wagle, S. Hosseinalipour, N. Khosravan, M. Chiang, and C. G. Brinton, “Embedding alignment for unsupervised federated learning via smart data exchange,” in _GLOBECOM 2022-2022 IEEE Global Communications Conference_. IEEE, 2022, pp. 492–497.
  * [134] S. Wagle, A. B. Das, D. J. Love, and C. G. Brinton, “A reinforcement learning-based approach to graph discovery in D2D-enabled federated learning,” in _GLOBECOM 2023-2023 IEEE Global Communications Conference_. IEEE, 2023, pp. 225–230.
  * [135] S. Wagle, S. Hosseinalipour, N. Khosravan, and C. G. Brinton, “Unsupervised federated optimization at the edge: D2D-enabled learning without labels,” _IEEE Transactions on Cognitive Communications and Networking_ , 2024.
  * [136] G. Lan, D.-J. Han, A. Hashemi, V. Aggarwal, and C. G. Brinton, “Asynchronous federated reinforcement learning with policy gradient updates: Algorithm design and convergence analysis,” _arXiv preprint arXiv:2404.08003_ , 2024.
  * [137] Z.-L. Chang, S. Hosseinalipour, M. Chiang, and C. G. Brinton, “Asynchronous multi-model dynamic federated learning over wireless networks: Theory, modeling, and optimization,” _IEEE Transactions on Cognitive Communications and Networking_ , 2024.
  * [138] J. Bian and J. Xu, “Accelerating asynchronous federated learning convergence via opportunistic mobile relaying,” _IEEE Transactions on Vehicular Technology_ , 2024.
  * [139] X. Zhou, W. Liang, A. Kawai, K. Fueda, J. She, I. Kevin, and K. Wang, “Adaptive segmentation enhanced asynchronous federated learning for sustainable intelligent transportation systems,” _IEEE Transactions on Intelligent Transportation Systems_ , 2024.
  * [140] W. Zhang, D. Yang, C. Zhang, Q. Ye, H. Zhang, and X. Shen, “(Com)2Net: A novel communication and computation integrated network architecture,” _IEEE Network_ , 2024.
  * [141] J. Chen, K. Li, and S. Y. Philip, “Privacy-preserving deep learning model for decentralized vanets using fully homomorphic encryption and blockchain,” _IEEE Trans. Intell. Transp. Syst._ , Aug. 2021.
  * [142] A. Nedić, A. Olshevsky, and M. G. Rabbat, “Network topology and communication-computation tradeoffs in decentralized optimization,” _Proc. IEEE_ , vol. 106, no. 5, pp. 953–976, Apr. 2018.
  * [143] S. Boyd, A. Ghosh, B. Prabhakar, and D. Shah, “Randomized gossip algorithms,” _IEEE Trans. Inf. Theory_ , vol. 52, no. 6, pp. 2508–2530, Jun. 2006.
  * [144] D. Kempe, A. Dobra, and J. Gehrke, “Gossip-based computation of aggregate information,” in _44th Annual IEEE Symposium on Foundations of Computer Science, 2003. Proceedings._ IEEE, 2003, pp. 482–491.
  * [145] A. Koloskova, S. Stich, and M. Jaggi, “Decentralized stochastic optimization and gossip algorithms with compressed communication,” in _International Conference on Machine Learning_. PMLR, 2019, pp. 3478–3487.
  * [146] C. Hu, J. Jiang, and Z. Wang, “Decentralized federated learning: A segmented gossip approach,” _arXiv preprint arXiv:1908.07782_ , 2019.
  * [147] A. Nedic, “Asynchronous broadcast-based convex optimization over a network,” _IEEE Trans. Automat. Contr._ , vol. 56, no. 6, pp. 1337–1351, Sep. 2010.
  * [148] T. C. Aysal, M. E. Yildiz, A. D. Sarwate, and A. Scaglione, “Broadcast gossip algorithms for consensus,” _IEEE Trans. Signal Process._ , vol. 57, no. 7, pp. 2748–2761, Feb. 2009.
  * [149] A. Bellet, R. Guerraoui, M. Taziki, and M. Tommasi, “Personalized and private peer-to-peer machine learning,” in _International conference on artificial intelligence and statistics_. PMLR, 2018, pp. 473–481.
  * [150] S. Wang, D. Li, J. Geng, Y. Gu, and Y. Cheng, “Impact of network topology on the performance of DML: Theoretical analysis and practical factors,” in _IEEE INFOCOM 2019-IEEE conference on computer communications_. IEEE, 2019, pp. 1729–1737.
  * [151] G. Neglia, G. Calbi, D. Towsley, and G. Vardoyan, “The role of network topology for distributed machine learning,” in _IEEE INFOCOM 2019-IEEE Conference on Computer Communications_. IEEE, 2019, pp. 2350–2358.
  * [152] O. Marfoq, C. Xu, G. Neglia, and R. Vidal, “Throughput-optimal topology design for cross-silo federated learning,” _Advances in Neural Information Processing Systems_ , vol. 33, pp. 19 478–19 487, 2020.
  * [153] F. Malandrino and C. F. Chiasserini, “Federated learning at the network edge: When not all nodes are created equal,” _IEEE Communications Magazine_ , vol. 59, no. 7, pp. 68–73, 2021.
  * [154] V. P. Chellapandi, A. Upadhyay, A. Hashemi, and S. H. Żak, “Decentralized federated learning: Model update tracking under imperfect information sharing,” _arXiv preprint arXiv:2403.13247_ , 2024.
  * [155] T. Lesort, V. Lomonaco, A. Stoian, D. Maltoni, D. Filliat, and N. Díaz-Rodríguez, “Continual learning for robotics: Definition, framework, learning strategies, opportunities and challenges,” _Inf. Fusion_ , vol. 58, pp. 52–68, Jun. 2020.
  * [156] M. Delange, R. Aljundi, M. Masana, S. Parisot, X. Jia, A. Leonardis, G. Slabaugh, and T. Tuytelaars, “A continual learning survey: Defying forgetting in classification tasks,” _IEEE Trans. Pattern Anal. Mach. Intell._ , Feb. 2021.
  * [157] M. F. Criado, F. E. Casado, R. Iglesias, C. V. Regueiro, and S. Barro, “Non-IID data and continual learning processes in federated learning: A long road ahead,” _Inf. Fusion_ , vol. 88, pp. 263–280, Dec. 2022.
  * [158] A. Usmanova, F. Portet, P. Lalanda, and G. Vega, “A distillation-based approach integrating continual learning and federated learning for pervasive services,” _arXiv preprint arXiv:2109.04197_ , 2021.
  * [159] T. J. Park, K. Kumatani, and D. Dimitriadis, “Tackling dynamics in federated incremental learning with variational embedding rehearsal,” _arXiv preprint arXiv:2110.09695_ , 2021.
  * [160] J. Yoon, W. Jeong, G. Lee, E. Yang, and S. J. Hwang, “Federated continual learning with weighted inter-client transfer,” in _International Conference on Machine Learning_. PMLR, 2021, pp. 12 073–12 086.
  * [161] R. Parasnis, S. Hosseinalipour, Y.-W. Chu, C. G. Brinton, and M. Chiang, “Connectivity-aware semi-decentralized federated learning over time-varying D2D networks,” _Proceedings of the Twenty-fourth International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing_ , 2023.
  * [162] A. H. Sayed, S.-Y. Tu, J. Chen, X. Zhao, and Z. J. Towfic, “Diffusion strategies for adaptation and learning over networks: an examination of distributed strategies and network behavior,” _IEEE Signal Process. Mag._ , vol. 30, no. 3, pp. 155–171, Apr. 2013.
  * [163] K. Chang, N. Balachandar, C. Lam, D. Yi, J. Brown, A. Beers, B. Rosen, D. L. Rubin, and J. Kalpathy-Cramer, “Distributed deep learning networks among institutions for medical imaging,” _J. Am. Med. Inform. Assoc._ , vol. 25, no. 8, pp. 945–954, Aug. 2018.
  * [164] M. J. Sheller, G. A. Reina, B. Edwards, J. Martin, and S. Bakas, “Multi-institutional deep learning modeling without sharing patient data: A feasibility study on brain tumor segmentation,” in _International MICCAI Brainlesion Workshop_. Springer, 2019, pp. 92–104.
  * [165] M. J. Sheller, B. Edwards, G. A. Reina, J. Martin, S. Pati, A. Kotrotsou, M. Milchenko, W. Xu, D. Marcus, R. R. Colen _et al._ , “Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data,” _Sci. Rep._ , vol. 10, no. 1, pp. 1–12, Jul. 2020.
  * [166] Y. Huang, C. Bert, S. Fischer, M. Schmidt, A. Dörfler, A. Maier, R. Fietkau, and F. Putz, “Continual learning for peer-to-peer federated learning: A study on automated brain metastasis identification,” _arXiv preprint arXiv:2204.13591_ , 2022.
  * [167] L. Yuan, Y. Ma, L. Su, and Z. Wang, “Peer-to-peer federated continual learning for naturalistic driving action recognition,” in _Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition_ , 2023, pp. 5249–5258.
  * [168] M. Assran, N. Loizou, N. Ballas, and M. Rabbat, “Stochastic gradient push for distributed deep learning,” in _International Conference on Machine Learning_. PMLR, 2019, pp. 344–353.
  * [169] A. G. Roy, S. Siddiqui, S. Pölsterl, N. Navab, and C. Wachinger, “Braintorrent: A peer-to-peer environment for decentralized federated learning,” _arXiv preprint arXiv:1905.06731_ , 2019.
  * [170] C. Pappas, D. Chatzopoulos, S. Lalis, and M. Vavalis, “Ipls: A framework for decentralized federated learning,” in _2021 IFIP Networking Conference (IFIP Networking)_. IEEE, 2021, pp. 1–6.
  * [171] Y. Shi, Y. Zhou, and Y. Shi, “Over-the-air decentralized federated learning,” in _2021 IEEE International Symposium on Information Theory (ISIT)_. IEEE, 2021, pp. 455–460.
  * [172] S. Chen, D. Yu, Y. Zou, J. Yu, and X. Cheng, “Decentralized wireless federated learning with differential privacy,” _IEEE Trans. Industr. Inform._ , vol. 18, no. 9, pp. 6273–6282, Jan. 2022.
  * [173] J. Wang, A. K. Sahu, G. Joshi, and S. Kar, “Matcha: A matching-based link scheduling strategy to speed up distributed optimization,” _IEEE Trans. Signal Process._ , vol. 70, pp. 5208–5221, Nov. 2022.
  * [174] J. Harding, G. Powell, R. Yoon, J. Fikentscher, C. Doyle, D. Sade, M. Lukuc, J. Simons, J. Wang _et al._ , “Vehicle-to-vehicle communications: readiness of V2V technology for application.” United States. National Highway Traffic Safety Administration, Tech. Rep., Aug. 2014.
  * [175] S. Samarakoon, M. Bennis, W. Saad, and M. Debbah, “Distributed federated learning for ultra-reliable low-latency vehicular communications,” _IEEE Trans. Commun._ , vol. 68, no. 2, pp. 1146–1159, Nov. 2019.
  * [176] Z. Du, C. Wu, T. Yoshinaga, K.-L. A. Yau, Y. Ji, and J. Li, “Federated learning for vehicular internet of things: Recent advances and open issues,” _IEEE Open J. Comput. Soc._ , vol. 1, pp. 45–61, May 2020.
  * [177] S. R. Pokhrel and J. Choi, “A decentralized federated learning approach for connected autonomous vehicles,” in _2020 IEEE Wireless Communications and Networking Conference Workshops (WCNCW)_. IEEE, 2020, pp. 1–6.
  * [178] Z. Yu, J. Hu, G. Min, H. Xu, and J. Mills, “Proactive content caching for internet-of-vehicles based on peer-to-peer federated learning,” in _2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS)_. IEEE, 2020, pp. 601–608.
  * [179] J.-H. Chen, M.-R. Chen, G.-Q. Zeng, and J.-S. Weng, “BDFL: a byzantine-fault-tolerance decentralized federated learning method for autonomous vehicle,” _IEEE Trans. Veh. Technol._ , vol. 70, no. 9, pp. 8639–8652, Aug. 2021.
  * [180] L. Barbieri, S. Savazzi, M. Brambilla, and M. Nicoli, “Decentralized federated learning for extended sensing in 6g connected vehicles,” _Veh. Commun._ , vol. 33, p. 100396, Jan. 2022.
  * [181] A. Nguyen, T. Do, M. Tran, B. X. Nguyen, C. Duong, T. Phan, E. Tjiputra, and Q. D. Tran, “Deep federated learning for autonomous driving,” in _2022 IEEE Intelligent Vehicles Symposium (IV)_. IEEE, 2022, pp. 1824–1830.
  * [182] D. Su, Y. Zhou, and L. Cui, “Boost decentralized federated learning in vehicular networks by diversifying data sources,” in _2022 IEEE 30th International Conference on Network Protocols (ICNP)_. IEEE, 2022, pp. 1–11.
  * [183] Y. Lu, X. Huang, Y. Dai, S. Maharjan, and Y. Zhang, “Federated learning for data privacy preservation in vehicular cyber-physical systems,” _IEEE Netw._ , vol. 34, no. 3, pp. 50–56, Jun. 2020.
  * [184] B. C. Tedeschini, S. Savazzi, R. Stoklasa, L. Barbieri, I. Stathopoulos, M. Nicoli, and L. Serio, “Decentralized federated learning for healthcare networks: A case study on tumor segmentation,” _IEEE Access_ , vol. 10, pp. 8693–8708, Jan. 2022.
  * [185] T. Nguyen, M. Dakka, S. Diakiw, M. VerMilyea, M. Perugini, J. Hall, and D. Perugini, “A novel decentralized federated learning approach to train on globally distributed, poor quality, and protected private medical data,” _Sci. Rep._ , vol. 12, no. 1, p. 8888, May 2022.
  * [186] S. Baghersalimi, T. Teijeiro, A. Aminifar, and D. Atienza, “Decentralized federated learning for epileptic seizures detection in low-power wearable systems,” _IEEE Transactions on Mobile Computing_ , 2023.
  * [187] Y. Qu, S. R. Pokhrel, S. Garg, L. Gao, and Y. Xiang, “A blockchained federated learning framework for cognitive computing in industry 4.0 networks,” _IEEE Transactions on Industrial Informatics_ , vol. 17, no. 4, pp. 2964–2973, 2020.
  * [188] T. Ranathunga, A. McGibney, S. Rea, and S. Bharti, “Blockchain-based decentralized model aggregation for cross-silo federated learning in industry 4.0,” _IEEE Internet of Things Journal_ , vol. 10, no. 5, pp. 4449–4461, 2022.
  * [189] O. Friha, M. A. Ferrag, M. Benbouzid, T. Berghout, B. Kantarci, and K.-K. R. Choo, “2DF-IDS: Decentralized and differentially private federated learning-based intrusion detection system for industrial IoT,” _Computers & Security_, vol. 127, p. 103097, 2023.
  * [190] W. Qiu, W. Ai, H. Chen, Q. Feng, and G. Tang, “Decentralized federated learning for industrial IoT with deep echo state networks,” _IEEE Transactions on Industrial Informatics_ , vol. 19, no. 4, pp. 5849–5857, 2022.
  * [191] M. Du, H. Zheng, X. Feng, Y. Chen, and T. Zhao, “Decentralized federated learning with markov chain based consensus for industrial IoT networks,” _IEEE Transactions on Industrial Informatics_ , vol. 19, no. 4, pp. 6006–6015, 2022.
  * [192] F. Wilhelmi, E. Guerra, and P. Dini, “On the decentralization of blockchain-enabled asynchronous federated learning,” _arXiv preprint arXiv:2205.10201_ , 2022.
  * [193] A. Koloskova, T. Lin, S. U. Stich, and M. Jaggi, “Decentralized deep learning with arbitrary communication compression,” in _International Conference on Learning Representations_ , 2019.
  * [194] Y. Belal, A. Bellet, S. B. Mokhtar, and V. Nitu, “Pepper: Empowering user-centric recommender systems over gossip learning,” _Proc. ACM Interact. Mob. Wearable Ubiquitous Technol._ , vol. 6, no. 3, pp. 1–27, Sep. 2022.
  * [195] Y. Xiao, Y. Ye, S. Huang, L. Hao, Z. Ma, M. Xiao, S. Mumtaz, and O. A. Dobre, “Fully decentralized federated learning-based on-board mission for uav swarm system,” _IEEE Communications Letters_ , vol. 25, no. 10, pp. 3296–3300, 2021.
  * [196] Z. Yan and D. Li, “Convergence time optimization for decentralized federated learning with leo satellites via number control,” _IEEE Transactions on Vehicular Technology_ , 2023.
  * [197] Z. Zhai, Q. Wu, S. Yu, R. Li, F. Zhang, and X. Chen, “FedLEO: An offloading-assisted decentralized federated learning framework for low earth orbit satellite networks,” _IEEE Transactions on Mobile Computing_ , 2023.
  * [198] D.-J. Han, S. Hosseinalipour, D. J. Love, M. Chiang, and C. G. Brinton, “Cooperative federated learning over ground-to-satellite integrated networks: Joint local computation and data offloading,” _IEEE Journal on Selected Areas in Communications_ , 2024.
  * [199] C. He, E. Ceyani, K. Balasubramanian, M. Annavaram, and S. Avestimehr, “Spreadgnn: Decentralized multi-task federated learning for graph neural networks on molecular data,” in _Proceedings of the AAAI Conference on Artificial Intelligence_ , vol. 36, no. 6, 2022, pp. 6865–6873.
  * [200] J. Han, Y. Ma, and Y. Han, “Demystifying swarm learning: A new paradigm of blockchain-based decentralized federated learning,” _arXiv preprint arXiv:2201.05286_ , 2022.
  * [201] C. Che, X. Li, C. Chen, X. He, and Z. Zheng, “A decentralized federated learning framework via committee mechanism with convergence guarantee,” _IEEE Transactions on Parallel and Distributed Systems_ , vol. 33, no. 12, pp. 4783–4800, 2022.
  * [202] Y. Chen, L. Liang, and W. Gao, “DFedSN: Decentralized federated learning based on heterogeneous data in social networks,” _World Wide Web_ , vol. 26, no. 5, pp. 2545–2568, 2023.
  * [203] J. Achiam, S. Adler, S. Agarwal, L. Ahmad, I. Akkaya, F. L. Aleman, D. Almeida, J. Altenschmidt, S. Altman, S. Anadkat _et al._ , “GPT-4 technical report,” _arXiv preprint arXiv:2303.08774_ , 2023.
  * [204] V. Kersic and M. Turkanovic, “A review on building blocks of decentralized artificial intelligence,” _arXiv preprint arXiv:2402.02885_ , 2024.
  * [205] H. Su, C. Xiang, and B. Ramesh, “Towards confidential chatbot conversations: A decentralised federated learning framework,” _The Journal of The British Blockchain Association_ , 2024.
  * [206] Z. Qin, D. Chen, B. Qian, B. Ding, Y. Li, and S. Deng, “Federated full-parameter tuning of billion-sized language models with communication cost under 18 kilobytes,” _arXiv preprint arXiv:2312.06353_ , 2023.