11institutetext:  Cardiff University, School of Computer Science &
Informatics, Cardiff, UK
11email: {belarbio, spyridopoulost<EMAIL_ADDRESS>22institutetext:
Toshiba Europe Ltd., Bristol Research & Innovation Laboratory, Bristol, UK
22email: {aftab.khan, pietro.carnelli<EMAIL_ADDRESS>
# Federated Deep Learning for Intrusion Detection in IoT Networks

Othmane Belarbi  0000-0002-6106-7669 11 Theodoros Spyridopoulos
0000-0001-7575-9909 11 Eirini Anthi  0000-0002-5274-0727 11 Ioannis Mavromatis
0000-0002-3309-132X 22 Pietro Carnelli  0000-0002-4993-5873 22 Aftab Khan
0000-0002-3573-6240 22

###### Abstract

The vast increase of Internet of Things (IoT) technologies and the ever-
evolving attack vectors and threat actors have increased cyber-security risks
dramatically. Novel attacks can compromise IoT devices to gain access to
sensitive data or control them to deploy further malicious activities. The
detection of novel attacks often relies upon Artificial Intelligence (AI)
solutions. A common approach to implementing AI-based Intrusion Detection
System (IDS) in distributed IoT systems is in a centralised manner. However,
this approach may violate data privacy and secrecy. In addition, centralised
data collection prohibits the scale-up of IDSs. Therefore, intrusion detection
solutions in IoT ecosystems need to move towards a decentralised direction.
Federated Learning (FL) has attracted significant interest in recent years due
to its ability to perform collaborative learning while preserving data
confidentiality and locality. Nevertheless, most FL-based IDS for IoT systems
are designed under unrealistic data distribution conditions. To that end, we
design an experiment representative of the real world and evaluate the
performance of two FL IDS implementations, one based on Deep Neural Networks
and another on our previous work on Deep Belief Networks. For our experiments,
we rely on TON-IoT, a realistic IoT network traffic dataset, associating each
IP address with a single FL client. Additionally, we explore pre-training and
investigate various aggregation methods to mitigate the impact of data
heterogeneity. Lastly, we benchmark our approach against a centralised
solution. The comparison shows that the heterogeneous nature of the data has a
considerable negative impact on the model performance when trained in a
distributed manner. However, in the case of a pre-trained initial global FL
model, we demonstrate a performance improvement of over $20\%$ (F1-score) when
compared against a randomly initiated global model.

###### Keywords:

Federated Learning Intrusion Detection System Internet of Things Deep Learning
Deep Belief Networks

##  1 Introduction

The increasing adoption of IoT devices in industrial applications, smart home
deployments and public infrastructure and the ever-evolving attack vectors and
threat actors have increased cyber-security risks dramatically. The complexity
and heterogeneity of these systems exacerbate the impact of an attack, ranging
from data theft to service disruption and human injury, rendering the security
of IoT devices a significant task [18].

In an attempt to enhance the security of IoT devices, several studies have
focused on AI to build more sophisticated threat detection models. Traditional
IDS solutions base their operations on Machine Learning (ML) models trained
centrally in the Cloud and then deployed across multiple devices.
Nevertheless, this centralised approach often suffers from increased network
overheads and high latency [8], thereby resulting in slow detection of
malicious traffic, lack of scalability and unresponsiveness to attacks in the
worst case. In addition, the centralised collection of system data can result
in data privacy and secrecy violations. These challenges urge the need to
develop effective IDSs tailored to the requirements of modern IoT systems.

In 2017, Researchers at Google introduced FL, a distributed learning paradigm
that preserves user privacy and data confidentiality [21]. In FL, multiple
clients collaboratively train a global model under the orchestration of a
central server without sharing any raw data. FL can be used to implement a
distributed IDS addressing the aforementioned requirements [2]. However, the
authors in [5] note that most intrusion detection research using FL utilises
unrealistic data distribution among the participants and inappropriate
datasets.

To address these issues, this paper presents an FL-based IDS tailored to IoT
systems, considering the impact of non-Independent and Identically Distributed
(non-IID) data. In particular, we use the recent TON-IoT [23] dataset. We
partition the dataset according to the destination IP address to simulate a
real-world scenario. This enables the performance evaluation of our FL-based
IDS with realistic non-IID data. We implement two FL-based IDS flavours, one
based on DNNs and one based on our previous work on DBNs and evaluate their
performance under the FL framework. To address the impact of the non-IID
dataset on the detection performance, we evaluate the effect of three
aggregation methods (FedAvg, FedProx and FedYogi) and also compare the use of
pre-trained models against randomly initialised models. We implement our FL
experiments relying on the Flower framework [4]. The experimental results show
that starting FL from a pre-trained model alleviates the effect of data
heterogeneity and improves the stability of global aggregation in FL. In
addition, FedProx increases the detection performance, followed closely by
FedYogi. Nevertheless, the centralised IDS performs significantly better than
the FL approaches.

The contributions of this work are summarised as follows:

  * –

We design a realistic set-up for FL-based IDS experimentation, relying on
real-world network traffic (TON-IoT) split based on the device IP address. Our
set-up results in a non-IID distributed dataset that reflects the
heterogeneity of IoT networks.

  * –

We implement and evaluate the performance of a DBN-based IDS and compare it
against a DNN-based IDS and a centralised IDS approach.

  * –

We evaluate the impact of pre-training in FL-based IDSs on the detection
performance given our realistic non-IID set-up.

  * –

We evaluate the effect of three aggregation methods (FedAvg, FedProx and
FedYogi) on the performance of FL-based IDSs in non-IID set-ups.

The remaining part of the paper proceeds as follows: In Section 2, we
highlight recent relevant work on FL-based IDS in IoT systems. Section 3
describes our methodology, including data partitioning and pre-processing. In
Section 4, we report the results of our experiments. Finally, we conclude the
paper and present pathways for future work in Section 5.

##  2 Related Work

In this section, we introduce the technical background related to FL and
analyse the latest research on intrusion detection in IoT using FL.

###  2.1 Federated Learning for IoT systems

FL is a distributed learning paradigm where multiple clients collaboratively
train a global model under the orchestration of a central server without
sharing raw data with a third party [21]. Instead of exchanging their local
data with the central server, the clients share only their model updates
(e.g., weight updates, gradient updates), hence preserving the data locality
and privacy [21].

In principle, the training process consists of two major phases: local
training and global aggregation. Initially, the clients download an initial
common global model from the central server. Upon retrieving it, each client
trains an updated model based on their local data. Then, these selected
clients send back their model updates to the central server. Finally, the
central server aggregates these updates (typically by averaging) and generates
a new global model. This process is repeated for multiple iterations until the
desired performance is achieved. Since the raw data is not shared for the
training in FL settings, the leakage of sensitive information is minimised,
and data privacy/secrecy is enhanced. Also, the communication overhead is
significantly reduced as there is no need for the clients to transmit their
raw data to the central server over the network [21]. Hence, FL enables the
deployment of IoT IDS more securely and efficiently [1].

###  2.2 FL-based IDS for IoT

In recent years, the application of FL in IoT networks has attracted
increasing interest among researchers. The characteristics of FL seem to offer
various benefits for IoT security systems. This section presents the relevant
works that research the use of FL for the specific task of intrusion detection
in IoT networks.

Various intrusion detection datasets have been used to develop FL-based IDS.
The NSL-KDD dataset is used by [26], to train a multi-class classification
Neural Network (NN). The authors consider three scenarios with different data
distributions. The experimental results of the FL based IDS showed that the FL
approach outperforms the distributed learning setting and can reach comparable
accuracy with respect to the centralised learning setting. Also, based on the
NSL-KDD dataset, [2] uses Multi-Layer Perceptron (MLP) to implement an FL
based IDS. The proposed approach is based on mimic learning where a teacher
model eliminates training data noise/error and soft labels are passed to the
student model as regularization to avoid overfitting. In [7], the authors
propose a new aggregation method named FedAGRU. The latter uses an attention
mechanism to prevent the uploading of unimportant model updates that do not
benefit the global model. The experiments conducted on the CICIDS2017 dataset
indicate that FedAGRU has higher detection accuracy while reducing
communication overhead compared to FedAvg. Finally, [14] evaluated an FL-based
Cloud intrusion detection scheme using the KDDCup’99 dataset. The authors
explored the utilisation of blockchain technology to secure the aggregation of
the model updates. However, such an approach requires resources that are
typically not available in the resource-constrained environment of an IoT
system.

While the majority of the works mentioned above have been designed for IoT
scenarios, a key limitation is that they are all based on intrusion datasets
which do not fully reflect the IoT-specific network traffic. Moreover, the
majority of the IDS datasets were not designed to be used in an FL
environment. To that end, recent efforts consider IoT-specific datasets to
develop FL-based IDS. For example, [22] evaluated an FL-based anomaly
detection for IoT security attacks using the Modbus dataset [9]. The authors
used the Gated Recurrent Units models for identifying the attacks, and the
federated averaging algorithm as the aggregation method. The evaluation
results show that the suggested approach outperforms the non-FL versions in
terms of accuracy in attack detection as well as privacy of user data.
Furthermore, [25] presented a federated deep learning method for zero-day
botnet attack detection in IoT-edge devices. The adopted method used the Bot-
IoT and N-BaIoT datasets to simulate the zero-day botnet attack and the DNN
model to classify the network traffic. One network traffic class was not
included in each partition to reproduce a real-life scenario. The FL-based IDS
were compared with the centralised, distributed and localised methods. The
results demonstrated that the FL method is most efficient for the detection of
zero-day attacks in IoT-edge devices against all other methods.

However, the aforementioned studies either do not provide data distributions
among the clients, or they simulate scenarios where the client’s data
distribution does not fluctuate, and all data samples follow the same
probability distribution. In realistic FL scenarios, non-IID data distribution
is common when data is generated by the end devices. Additionally, recent
studies [20, 30] have established that training on non-IID data introduces
challenges that could deteriorate the performance of the IDS across the entire
network, and lead to convergence issues. On this question, there is a
relatively small body of literature that considers the aspect of non-IID in
their evaluation of FL-based IDS.

The researchers in [16] proposed LocKedge, a multi-attack detection mechanism
for deployment at the edge. The LocKedge system is based on a NN model
together with Principal Component Analysis (PCA) for feature engineering. The
suggested method split the dataset into 4 partitions according to the
attackers’ IP address. Similarly, the authors in [8, 28] evaluated the impact
of non-IID data on the development of FL-based IDS for IoT networks. These
studies confirmed the finding about the performance reduction of FL in non-IID
scenarios. However, they do not provide information on the implementation
being used and data distribution among the different clients. To this end, we
designed an experimental set-up with the necessary non-IID characteristics
presented in Section 3.

In most of the aforementioned works, the model’s parameters are initialised
with random weights. However, a growing body of research [6, 24] has explored
the impact of pre-training and initialisation on FL, especially in non-IID
scenarios, suggesting that starting from a pre-trained model could reduce both
the effect of data heterogeneity and the training time required to reach the
final model. Our work contributes to the body of knowledge by exploring the
impact of pretraining in FL-based IDS. In addition, we experiment with various
aggregation methods to address the impact of non-IID distributed data.

##  3 Methodology

In this section, we describe the key processes of our FL-based IDS for IoT.
These include the data distribution among multiple FL clients, the on-the-
device data preparation, and the learning process.

###  3.1 Dataset

Table 1: Statistics of the original TON-IoT dataset. N° |  Label |  Total Data Record |  Ratio(%)
---|---|---|---
1 |  Scanning |  7,140,161 |  31.96
2 |  DDoS |  6,165,008 |  27.6
3 |  DoS |  3,375,328 |  15.11
4 |  XSS |  2,108,944 |  9.44
5 |  Password |  1,718,568 |  7.69
6 |  Normal |  796,380 |  3.56
7 |  Backdoor |  508,116 |  2.27
8 |  Injection |  452,659 |  2.03
9 |  Ransomware |  72,805 |  0.33
10 |  MITM |  1,052 |  0.0047

The selection of an appropriate dataset is a critical component to consider
while designing our FL-based IDS for IoT devices. There are multiple IDS
datasets available; however, the majority of them are unsuitable in the
context of FL. Their main problem is that they cannot be properly disseminated
among the different clients in a non-IID fashion [28]. As a result, our
suggested approach focuses on IoT datasets for IDS that can be partitioned
based on the destination IP address, namely MedBIoT [11], IoT Benign & Attack
Traces [13], BoT-IoT [17] and TON-IoT [23]. In particular, we decided to use
the TON-IoT dataset introduced by the University of New South Wales, Canberra
[23]. It presents the best ratio between benign and attack traffic compared to
the other IoT-focused datasets. The TON-IoT dataset is based on a realistic
testbed that includes heterogeneous data sources and a wide range of attack
types, allowing it to give a comprehensive evaluation of IoT security systems.
Table 1 describes the data samples found in the TON-IoT dataset. The dataset
has nine types of attacks and is prone to high-class imbalance.

###  3.2 Data Partitioning

Our data partitioning strategy consists of three steps. First, we group the
data samples according to the destination IP address. Then, we select the top
ten IP addresses with the highest number of samples. Finally, the samples of
each of these IP addresses are assigned to a single FL client. Each client
corresponds to one of the top ten IP addresses, as shown in Figure 1. We used
only the top ten IP addresses to avoid having clients with very minimal data
samples.

Table 2 describes the data distribution among the ten different clients after
partitioning and preprocessing as described below. Such distribution allows us
to have the most realistic scenario with a high degree of non Independent and
Identically Distributed (IID)-ness.

Table 2: Description of the class distribution for each client after pre-
processing.

Client | # Samples | Scan | DDoS | XSS | Pwd | DoS | Normal | Back | Injection | Rans | MITM
---|---|---|---|---|---|---|---|---|---|---|---
1 | 3,786,885 | 815 | 3,502,650 | 576 | 26,460 | 192,130 | 16,202 | - | 48,052 | - | -
2 | 2,740,330 | 636,963 | 993,069 | 285,436 | 278,833 | 303,583 | 149,315 | - | 93,129 | - | 2
3 | 2,535,874 | 1,167,320 | 6,906 | 344,136 | 864,611 | 13,108 | 48,046 | - | 91,740 | - | 7
4 | 1,984,149 | 568,501 | 465,525 | 280,915 | 95,029 | 399,568 | 79,285 | - | 95,323 | - | 3
5 | 1,378,579 | 13,382 | 630,127 | 604,691 | 2,883 | 86,721 | 27,304 | 18 | 12,889 | - | 564
6 | 1,165,842 | 1,161,124 | 210 | - | - | - | 4,508 | - | - | - | -
7 | 1,008,113 | 412,493 | 452,559 | - | 4,444 | 116,463 | 22,154 | - | - | - | -
8 | 680,480 | 680,446 | - | - | - | - | 22 | - | - | - | 12
9 | 603,465 | 336,770 | 3,642 | - | 37,835 | 181,710 | 21,745 | 133 | - | 21,629 | 1
10 | 425,249 | 384 | - | - | - | - | - | 423,122 | 3 | 1,737 | -

###  3.3 On-the-device Data Preparation

Real-world datasets suffer from various unwanted artefacts, which can be
attributed to discrepancies in the sensing protocols, hardware imperfections,
and sampling inconsistencies. Therefore, data pre-processing (i.e. data
cleaning and normalisation) is required prior to model training. To simulate a
realistic scenario where data are generated independently on each client, each
FL client has to go through data pre-processing individually and independently
from the other clients before local model training, as shown in Figure 1.

Figure 1: High-level architecture of our FL-based IDS.

####  3.3.1 Data Cleaning:

One of the major imperfections in intrusion datasets is missing data in
collected samples. To handle NaN values, we remove the rows containing missing
values since the dataset is large enough and the fraction of rows containing
missing values is small. Hence, it will have minimal effect on performance.
Moreover, we get rid of duplicate instances and samples with infinite values
for the same reasons.

After dropping the unwanted values, we removed unnecessary flow features such
as “ts”, “src_ip”, “src_port”, “dst_ip” and “dst_port” in order to ensure that
the model learns the complexity of the flow properties instead of the
particular endpoints responsible for each traffic, and avoid overfitting. We
also encoded the categorical features with ordinal encoding and standardised
the numerical ones.

We then split the local on-the-device dataset into training and testing sets
in the proportions of $80$%, and $20$% respectively, as shown in Figure 1. A
stratified split is crucial in datasets with high-class imbalance since it
permits a more accurate evaluation of the model’s performance [12]. By
maintaining the number of samples for each class, stratified data splitting
effectively ensures sufficient samples of the minority class in the training
set.

####  3.3.2 Data Normalisation:

Most detection algorithms only accept numerical input values. Therefore, we
convert the categorical features into numerical features. To increase model
performance, we normalise the whole set of numerical features without
distorting disparities in the ranges of values. This stage is vital for
removing any bias from raw data and optimising model training.

It is important to note that in order to preserve privacy, we perform the
normalisation on each FL client independently from the other clients. Table 2
describes the final dataset on each client after partitioning and
preprocessing

###  3.4 Federated Learning Training

The server is in charge of coordinating the federated clients’ training
efforts. It is responsible for aggregating the model updates transmitted by
the clients to generate a new global model.

Initialising the weights of the Global model is also the server’s
responsibility. Once completed, the initial model is distributed to all
clients, and the training process can begin. Each client then trains this
model with its own local data, computes a new set of weights, and transmits
the model updates to the server. After receiving the updated model parameters,
the server combines them using an aggregation function to produce an updated
global model and distributes it to the clients. This process is repeated
several times until the model is converged.

###  3.5 Deep Belief Networks

Figure 2: DBN architecture.

In our experiments, we used a DNN and a DBN as the basic models for our FL. In
this section, we briefly discuss the structure of a DBN.

DBN is a generative model, composed of stacked layers of Restricted Boltzmann
Machine (RBM) as shown in Fig. 2. An RBM is an undirected energy-based model
that consists of two layers, namely visible and hidden layer [29]. The
training process of a DBN model requires two phases: unsupervised layer-by-
layer training and supervised fine-tuning. During unsupervised pre-training,
except for the first and last layer, each RBM layer is trained one by one
using the contrastive divergence algorithm [15], and the output of each RBM is
used as input for the subsequent RBM layer. In the second phase, all weights
are fine-tuned by using back-propagation. A major advantage of DBN is that it
can establish a meaningful pattern for the attacks outperforming traditional
models. For more information on DBNs please refer to our previous work in [3]

##  4 Experiments

All the experiments were conducted using an 8-core CPU with an Apple M2 chip
with 16GB RAM in a macOS environment. The building, training, and evaluation
of the Deep Learning (DL) model were performed using the Pandas, NumPy,
Scikit-Learn, and PyTorch libraries.

###  4.1 Model Training

Table 3: Model architecture and hyperparameters.

(a) DBN Attribute |  Value
---|---
Input/Output Layer |  38:10
Hidden Layer |  100:150:200:50
Hidden Layers Act. Fct. |  ReLu
Output Layer Act. Fct. |  SoftMax
Optimization Algorithm |  Adam
Training Loss Function |  Cross Entropy
Momentum |  0.9
Weight init. |  Xavier initialiser
Bias init. |  Zeros (0)
Gibbs step |  1 step

(b) DNN Attribute |  Value
---|---
Input/Output Layer |  38:10
Hidden Layer |  128:128:64
Hidden Layers Act. Fct. |  ReLu
Output Layer Act. Fct. |  SoftMax
Optimization Algorithm |  SGD
Training Loss Function |  Cross Entropy

In this study, we employ a DBN and a DNN for multi-class classification. We
train the DNN and DBN models across 50 rounds and two epoch for each training
round. Ten clients with highly skewed data distributions are collaboratively
used to train the global model. Since non-IID distributed datasets may
deteriorate the performance FedAvg [20, 30], we experiment with different
aggregation algorithms including FedProx [19] and FedYogi [27]. These
aggregation methods have demonstrated more robust convergence than FedAvg,
particularly in highly heterogeneous settings [19, 27]. Moreover, we compare
random Global model initialisation against pre-trained model initialisation.
We initialise the model with pre-trained weights on the server side. The pre-
trained weights are obtained using the data samples from the rest of the
destination IP addresses that were excluded from the experimentation set-up as
described in Section 3.2. The architecture and hyperparameters of the DBN and
DNN are shown in Table 3.

###  4.2 Evaluation Metrics

Depending on the type of dataset and its class distribution, a suitable set of
metrics is required to assess a given model’s performance reliably. In the
case of intrusion datasets that mainly suffer from high-class imbalance, the
accuracy metric cannot be relied upon [10]. Therefore, we chose the F1-score,
Precision, and Recall as our metrics to evaluate the model. Furthermore, given
the unequal distribution of the data based on the label, the weighted average
is used in order to consider the size of each class. This is an important
consideration, as it ensures that the performance of the model is evaluated
fairly, taking into account the distribution of instances in each class.

Figure 3: Confusion Matrices.

###  4.3 Comparative Results

This section presents the experimental results based on the evaluation metrics
defined in the previous section. Upon reviewing the literature, no study of
FL-based IDS on non-IID data and reproducible methodology was found.
Therefore, we could not compare our results against other works. Figure 3
shows the confusion matrices of the DNN and DBN with three different
scenarios, namely centralised learning, FL and pre-trained FL. The correct
classifications are represented on the main diagonal of the confusion matrix
whereas any off-diagonals denote the erroneously categorised traffic. As a
result, the best model will consist of a confusion matrix with only diagonal
elements and the rest of the elements near zero.

(a) DNN

(b) DBN

Figure 4: Accuracy and F1-score performance for 50 rounds and evaluation of
three FL strategies (FedAvg, FedYogi, FedProx).

The results obtained from the simulations are summarised in Figure 4. What
stands out in these figures is the clear improvement in performance for the
FedYogi and FedProx algorithms compared to FedAvg. These differences can be
explained in part by their ability to tackle data heterogeneity. Furthermore,
the FedProx simulation presents better convergence stability and prevents
divergence through its regularisation hyperparameter.

PrecisionRecallF1-Score$20$$40$$60$$80$$100$$91$$74$$78$$76$$61$$64$$57$$53$$54$CentralisedPre-
trained FLFL

(a) DNN

PrecisionRecallF1-Score$20$$40$$60$$80$$100$$97$$93$$94$$77$$71$$72$$54$$37$$42$CentralisedPre-
trained FLFL

(b) DBN

Figure 5: F1-score, Precision and Recall achieved by the proposed multi-attack
classification for the different approaches.

Figure 5 depicts the averaged Precision, Recall, and F1-score achieved for the
different approaches. On the one hand, we can observe that the centralised
approach can detect intrusions with high Precision, Recall and F1-score. It
achieves the best results with $97$% Precision, $93$% Recall and $94$%
F1-score. However, as mentioned earlier, the centralised approach requires the
centralisation of a large amount of data, introducing significant delay at the
pre-processing stage and making it easier for intruders to leak sensitive
information.

On the other hand, the performance of the FL-DBN model with random global
initialisation correctly classified the network traffic with only $54$%
Precision, $37$% Recall and $42$% F1-score, while the FL-DNN model achieved
$57$% Precision, $53$% Recall and $54$% F1-score. The model performance
reduces significantly when the local data is non-IID by up to $50$%. As
described by [30], training on non-IID data introduces challenges that
deteriorate the performance of the IDS and lead to convergence issues. The
performance reduction can be explained by the weight divergence of each
participant. To mitigate this issue, we propose to start FL from a pre-trained
model in order to reduce the effect of data heterogeneity and increase the
stability of global aggregation in FL. We can see that the performance results
are significantly improved over FL with random initialisation. Indeed, the
pre-trained FL-DBN and FL-DNN models have an F1-score of $72\%$ and $62\%$
respectively. Thus, it demonstrates that the pre-trained model enables the
training of more accurate FL-based IDS (up to $30$%) than is possible when
starting with random initialisation.

##  5 Conclusion and Future Work

The purpose of the current study was to design a multi-class FL-based IDS for
IoT and evaluate it under realistic non-IID conditions. Our experimental
results confirmed that training an FL-based IDS on heterogeneous data, either
relying on DNNs or DBNs, can significantly reduce the model performance by up
to $50$%. As a mitigation, we investigated the utilisation of a pre-trained
Global model. We demonstrated that pre-training the Global model in FL can
address data heterogeneity to some extent. In addition, we demonstrated that
FedProx and FedYogi increase detection performance since they are designed to
address highly non-IID datasets.

A limitation of this work is that we consider a pre-trained model directly
available. However, it may not always be possible to get public data at the
server and pre-train the initial model. As such, exploring pre-training with
synthetic data would be a fruitful area for further work. Finally, it is
important to note that normalising the data on each client individually can
deteriorate the performance of the model. This could be improved by
calculating the normalisation parameters centrally, relying on the clients’
statistical characteristics. However, sharing the statistical parameters of
the local datasets can disclose valuable information about the client. Hence,
we plan to investigate in the future how we can achieve better normalisation
whilst preserving full data privacy.

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  *[IoT]: Internet of Things
  *[AI]: Artificial Intelligence
  *[IDS]: Intrusion Detection System
  *[IDSs]: Intrusion Detection System
  *[FL]: Federated Learning
  *[ML]: Machine Learning
  *[non-IID]: non-Independent and Identically Distributed
  *[DNNs]: Deep Neural Network
  *[DBNs]: Deep Belief Network
  *[DBN]: Deep Belief Network
  *[DNN]: Deep Neural Network
  *[NN]: Neural Network
  *[MLP]: Multi-Layer Perceptron
  *[PCA]: Principal Component Analysis
  *[IID]: Independent and Identically Distributed
  *[RBM]: Restricted Boltzmann Machine
  *[DL]: Deep Learning