[1]Elaheh Jafarigol [1]Data Science and Analytics Institute, University of
Oklahoma, 202 W. Boyd St., Room 409, Norman, 73019, Ok, USA 2]School of
Industrial and Systems Engineering, University of Oklahoma, 202 W Boyd St.,
Room 124, Norman, 73019, OK, USA

3]Department of Industrial Engineering, Isfahan University of Technology,
Isfahan, Iran

# Exploring Machine Learning Models for Federated Learning: A Review of
Approaches, Performance, and Limitations

<EMAIL_ADDRESS>Theodore B. Trafalis  Talayeh Razzaghi  MonaZamankhani
*  [  [

###### Abstract

In the growing world of artificial intelligence, federated learning is a
distributed learning framework enhanced to preserve the privacy of
individuals’ data. Federated learning lays the groundwork for collaborative
research in areas where the data is sensitive. Federated learning has several
implications for real-world problems. In times of crisis, when real-time
decision-making is critical, federated learning allows multiple entities to
work collectively without sharing sensitive data. This distributed approach
enables us to leverage information from multiple sources and gain more diverse
insights. This paper is a systematic review of the literature on privacy-
preserving machine learning in the last few years based on the Preferred
Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.
Specifically, we have presented an extensive review of supervised/unsupervised
machine learning algorithms, ensemble methods, meta-heuristic approaches,
blockchain technology, and reinforcement learning used in the framework of
federated learning, in addition to an overview of federated learning
applications. This paper reviews the literature on the components of federated
learning and its applications in the last few years. The main purpose of this
work is to provide researchers and practitioners with a comprehensive overview
of federated learning from the machine learning point of view. A discussion of
some open problems and future research directions in federated learning is
also provided.

###### keywords:

Federated Learning, Privacy-preserving Machine Learning, Distributed Learning,
Supervised/Unsupervised Learning, Artificial Intelligence

##  1 Introduction

Privacy is the individuals’ right to control their personal information. With
the advances in data-driven technologies, ensuring privacy protection has
become more challenging. Privacy and federated learning are intertwined
concepts in machine learning. Privacy and federated learning are intertwined
concepts in machine learning. Federated learning offers a promising solution
by enabling collaborative model training without exposing sensitive data. This
decentralized approach preserves privacy by design, allowing organizations and
individuals to collaborate while minimizing the risk of data breaches or
unauthorized access. With privacy at its core, federated learning has become a
possible solution for scenarios where the data is sensitive and individual
privacy is a concern. An algorithm is considered private if the outcome of the
analysis is the same, whether any arbitrary individual data is part of the
dataset or not [1]. This definition is the basis for privacy protection
mechanisms in federated learning. Federated learning is a broad term that
includes different aspects of data collection, storage, analysis, and
communication in a decentralized information system where data centers can not
disclose data for learning purposes. The term federated learning was
introduced in the paper published based on the results of a research project
carried out at Google in 2016 for text input prediction on mobile devices [2].
The authors designed a collaborative environment for a group of devices
referred to as clients, coordinated by a central server, also known as a
service provider. They also conducted an empirical study on the model using
different model structures on benchmark datasets for image classification and
language processing applications.
The ideas behind federated learning have been around for decades, but thanks
to the abundant data that is available to us and advances in computation
power, we are able to efficiently train machine learning models on a network
of decentralized data sources. Advances in the field of machine learning,
especially deep learning, allow us to build powerful models that can
accurately analyze different data types and provide valuable insights. Without
the need to collect data in one place, we can leverage multiple sources of
data, knowing that individuals’ privacy is preserved while reducing the costs
of data storage and achieving high-quality results. Distributed machine
learning algorithms create an environment where data storage and training
happen on the group of distributed machines. However, the main difference
between distributed learning and federated learning is the notion of privacy,
and the advances in data analysis capacities made over the last decade allow
us to develop and deploy privacy-preserving algorithms at scale [3, 4, 5].
Federated learning is the process of training data locally and improving the
global model. In the federated learning framework, as shown in Figure 1, the
data is stored in local data centers, and limited information required for the
learning task is privately communicated with the central server.

Figure 1: The Federated Learning Framework [2]

This architecture is called a client-server design. If the data centers are
both responsible for the task of storage and aggregation, the architecture is
called peer-to-peer. Suppose $n$ is the total number of sample points. In that
case, $K$ is the total number of clients, $n_{k}$ is the total number of
sample points on client $k$, and $\eta$ is the learning rate. The goal of
federated learning is to minimize the objective function $f$, also known as
the loss function, where $f_{k}(\delta)$ is the loss function, and $\delta$ is
the evaluation value for the k-th client:

$f(\delta):=\displaystyle\sum_{k=1}^{K}\frac{n_{k}}{n}f_{k}(\delta)$ (1)

In this equation, $\delta$ is updated after each iteration until we reach the
optimal solution or the number of iterations set as the stopping criterion is
satisfied. This optimization problem is solved using a federated Stochastic
Gradient Descent (SGD) method, which is described in algorithm 1. This
algorithm shows that the gradient steps are taken by each client, and the
model parameters are calculated as $\delta_{t+1}\leftarrow\delta_{t}-\eta
g_{k}$.

Algorithm 1 Federated Stochastic Gradient Descent

$\delta_{t},\nabla f(\delta_{t}),n,n_{k},\eta$

$C<1$
$\triangleright$ a subset of clients is selected at each round

$\delta_{t}$:= the current state of the evaluation value

while $f(\delta_{t})$ $\neq$ optimal do

$g_{k}=\nabla f_{k}(\delta_{t})$
$\triangleright$ $g_{k}$ := the gradient of client k

$\delta_{t+l}\leftarrow\delta_{t}-\eta\nabla
f(\delta_{t})=\delta_{t}-\sum_{k=1}^{K}\frac{n_{k}}{n}g_{k}$
$\triangleright$ Aggregation of client gradients to create a new model at the
central server

end while

Federated learning affects the modeling step of the Cross-Industry Standard
Process for Data Mining (CRISP-DM), which starts from local data storage in
data centers to communicate with the central server to iteratively aggregate
the model parameters and update the global model to achieve the desired
learning accuracy in data centers.

Figure 2: Data Mining Process in Federated Learning Based on CRISP-DM Model

Figure 2 shows the Federated learning life cycle embedded in CRISP-DM.

##  2 Applications

Federated learning has been applied to various fields when the data is
sensitive and scattered across multiple servers or devices. In particular,
federated learning is a compelling approach in healthcare, IoT, and crisis
management, where information limitation is an issue.

### IoT

Industry 4.0 is the era of interconnected physical and digital technologies.
Through the fourth industrial revolution, smart operations evolved, and the
demand for informed and data-driven solutions increased. In the digital world,
data is constantly generated in texts, images, and measurements from thousands
of sensors and devices, which require powerful systems to perform extensive
computations for data processing. Smart cities, cloud-based technologies, edge
computing, and IoT need reliable, secure, real-time analysis tools. This has
increased the demand for systems that can address scalability,
interoperability, resource limitations, and privacy issues [6, 7].

Nguyen et al. [8] surveyed the application of federated learning to leverage
the data on IoT devices for smart cities and industries, leading to advances
in healthcare, transportation, and Unmanned Aerial Vehicles. Khan et al. [9]
surveyed different aspects of federated learning for IoT applications. The
authors compared and evaluated the methods from robustness, quantization,
sparsification, scalability, security, and privacy perspectives. Varlamis et
al. [10] used federated learning to find energy-saving solutions based on
sensor data. Federated learning offers several benefits when applied to
Internet of Things (IoT) applications such as:

  * •

Privacy preservation: This is the primary advantage of federated learning. IoT
devices often collect sensitive personal information, such as health data or
home automation preferences, and federated learning allows these devices to
train machine learning models without exposing individuals to possible harm
and misuse of data.

  * •

Reduced data transfer: IoT devices are often resource-constrained, making it
inefficient and costly to transmit large volumes of data to a central server.
By keeping data on the edge, federated learning reduces the need for extensive
data transfer and lowers communication overhead.

  * •

Edge computing: Federated learning fits well with edge computing, where data
processing occurs closer to the source (IoT devices) rather than in a data
center. Therefore, minimizing latency and improving real-time decision-making
which is crucial for applications like autonomous vehicles and industrial
automation.

Some of the challenges and limitations of federated learning in this domain
are:

  * •

Heterogeneity: IoT devices come in various shapes, sizes, and capabilities,
making them challenging to harmonize for federated learning. Ensuring that
models can be trained effectively across diverse IoT ecosystems is a
significant challenge. In federated learning, some devices may also have more
data or contribute more frequently to model updates than others. Managing the
data across IoT devices can affect the fairness and accuracy of the learned
models.

  * •

Communication overhead: Aggregating model updates from numerous IoT devices
can be challenging, especially when dealing with intermittent connectivity,
device failures, or adversarial behavior. Robust aggregation methods are
needed to handle these scenarios.

  * •

Computational overhead: Federated learning can be computationally intensive,
which may be problematic for resource-constrained IoT devices. Balancing model
training with energy efficiency and processing power is a limitation that
needs to be addressed.

  * •

Scalability: As the number of IoT devices increases, managing federated
learning across the network becomes more challenging.

Federated learning holds great promise for IoT applications. However,
addressing challenges and overcoming limitations is essential to fully harness
the potential of federated learning in the rapidly expanding IoT ecosystem.

### Healthcare

Federated learning is a promising approach for learning from healthcare data,
which is highly regulated and cannot be openly shared with the public.
Therefore, if privacy is ensured, it can significantly benefit from utilizing
artificial intelligence and machine learning to move towards personalized
healthcare and computer-aided diagnosis. Federated learning creates a global
model of decentralized data, such as the data from hospitals, labs, and
clinical trials without direct access to the data [11]. For instance, Feki et
al. [12] utilized a federated learning approach to build a powerful model to
classify COVID-19 X-rays based on data collected from multiple institutes.
Rieke et al. [13] explored the existing literature on federated learning for
healthcare with the challenges and open problems in digital healthcare. Some
of the benefits of federated learning in healthcare applications are:

  * •

Privacy preservation: Healthcare data is highly sensitive and subject to
strict privacy regulations. Federated learning enables healthcare institutions
to collaborate on model training without sharing raw patient data, preserving
privacy and compliance with regulations like HIPAA.

  * •

Large-scale data utilization: Without privacy concerns, healthcare
organizations can tap into a vast pool of data from various sources, including
hospitals, clinics, wearable devices, and electronic health records, to
advance machine learning and computer-aided diagnosis.

  * •

Personalized medicine: by leveraging patient-specific data, personalized
treatment plans become more viable, leading to more effective and tailored
healthcare interventions.

However, some of the challenges and limitations are:

  * •

Heterogeneity: Health data comes from various hospitals, clinics, personal
health monitoring devices, and more. Differences in data formats, quality,
completeness, and availability pose challenges for integrating the data and
achieving accurate and reliable results.

  * •

Regulatory compliance: Healthcare is heavily regulated, with different regions
and countries having their own sets of rules and standards. Navigating
regulatory compliance while implementing federated learning can be complex and
time-consuming.

  * •

Bias and fairness: Federated learning may inherit biases present in the data
from participating institutions, potentially leading to biased or unfair model
outcomes.

  * •

Model quality control: Ensuring the quality and consistency of models across
different institutions can be challenging. Mechanisms for model monitoring,
validation, and quality control are essential to maintain high standards of
care.

Federated learning is a promising approach for healthcare applications, and
addressing the challenges can have significant impacts on healthcare
operations.

### Crisis management

There is a growing interest in machine learning algorithms for weather
applications and natural disasters. Leveraging large data sets from multiple
resources facilitates collaborative learning from data collected at different
geographic regions, allowing for more powerful and precise models [14].
Bypassing the risks of data sharing encourages meteorological institutions
across the countries to harness the power of machine learning while ensuring
privacy and efficient utilization of resources, resulting in more accurate and
timely predictions. On a larger scale, when dealing with natural disasters and
emergencies, federated learning allows government agencies, international
organizations, local support groups, and communities to collaborate
effectively without privacy restrictions caused by traditional centralized
learning. Federated learning provides the framework for secure communication
of knowledge, resulting in early detection, risk assessment, and effective
emergency response strategies using more accurate and robust models. The
benefits of federated learning in crisis management are:

  * •

Privacy Preservation: In crisis management scenarios, sensitive and critical
data may be involved, such as location data, medical records, or disaster
response plans. Federated learning enables multiple entities to collaborate on
model training without sharing raw data, preserving privacy and security.

  * •

Real-time updates: Crisis management requires quick decision-making based on
the latest information. Federated learning enables real-time model updates as
new data becomes available, ensuring that decision support systems remain
current and effective during rapidly evolving situations.

  * •

Resource efficiency: Crisis response often involves distributed teams and
resources. Federated learning leverages the computing power of edge devices
and distributed data sources, minimizing the need for centralized data storage
and processing resources.

  * •

Customization for local conditions: Different regions may have unique
characteristics and needs during a crisis. Federated learning allows for
localized model customization, ensuring that solutions are tailored to
specific conditions and requirements.

Despite its benefits, the challenges and limitations of this approach are:

  * •

Data availability: Federated learning relies on the availability of data.
During a crisis when data may be sparse, incomplete, or unreliable due to
infrastructure damage or connectivity issues, training reliable models becomes
challenging.

  * •

Heterogeneity: In addition to data availability, collecting data from multiple
sources such as governmental and private organization databases, social media,
and on-site observations results in inconsistencies in data formats that need
special attention during model training and interpretation of results.

  * •

Model drift: In dynamic crisis situations, data distributions can change
rapidly, causing model drift which requires the model to be continuously
updated and retrained.

  * •

Communication overhead: Connectivity issues and limitations on bandwidth are
possible issues during a crisis. Allocating adequate resources and careful
planning prior to emergency scenarios prevents disruptions in critical
operations due to disconnections in the network.

Overall, federated learning offers significant potential for enhancing crisis
management applications and improving the effectiveness of disaster mitigation
and recovery efforts.
Figure 3 shows some of the applications of federated learning in the industry
based on the papers reviewed in this work.

Figure 3: Applications of Federated Learning in Industry

###  2.1 Contributions

There is a rich body of research on different aspects of federated learning,
such as data processing, learning models, aggregation methods, specifications
of data centers and the central server, communication security, and efficiency
among the elements [15] and the multiple software and libraries used for
implementing federated learning[16]. In this work, we survey the existing
research on federated learning. What distinguishes this work from other
surveys is the focus on the model selection aspect of the federated learning
process, complementing other recent surveys [17, 18, 19, 20, 21, 22]. We
explore the different machine learning models used in federated learning to
tackle problems in different domains. In this survey, we have investigated the
papers published between 2016-2022 in accredited peer-reviewed journals and
conferences and classified them based on the machine learning methods used for
learning. We limited the search to the keywords federated learning, privacy-
preserving machine learning, distributed learning, supervised/unsupervised
learning, and artificial intelligence. The PRISMA diagram presented in Figure
4 demonstrates the searching strategy in this survey.

Figure 4: PRISMA Flow Diagram Summarizing the Search Strategy

##  3 Components of Federated Learning

In the following sections, we will provide a detailed explanation of the
federated learning framework’s components: storage, privacy, communication,
federated aggregation, and privacy-preserving machine learning, respectively.
We dive into a detailed discussion of different machine learning models used
for training decentralized data, their use cases and applications, and some
technical details useful for implementation.

###  3.1 Storage

Federated learning is a cross-organizational framework. Therefore, the
features and observations may vary between different data centers. Depending
on the architecture of the data centers and how the data are partitioned,
three scenarios of horizontal partitioning, vertical partitioning, and
transfer learning have been discussed.

#### Horizontal partitioning

Horizontal federated learning, also known as sample-based federated learning,
is the scenario in which the data centers have the same features but different
sample spaces that require modifications to the training model[23]. For
example, a network of local banks that individually collect a certain list of
information from their clients. In this example, the clients are different.
Therefore, the sample space varies between the banks. Horizontal federated
learning allows entities to build a generalized global model based on a larger
data pool without compromising privacy.

#### Vertical partitioning

Vertical federated learning, also known as feature-based federated learning,
is when local datasets have the same sample space but differ in their
features. Numerous researchers have explored training models specified for
vertically partitioned data. For example, a local bank and an insurance
company share the same clients but collect different types of information. If
the two entities were to build a local model collaboratively, the two datasets
would have common sample space but very different features. The common
aggregating methods aren’t effective in vertically partitioned data.
Therefore, the difference between the feature spaces causes different
challenges and creates opportunities for further research to address the issue
of fault diagnosis.

#### Transfer Learning

Transfer Learning is the learning structure in which the local datasets differ
both in the sample and feature space. Thus, knowledge is derived from various
sources to achieve a global model. Despite all the challenges, transfer
learning has been applied to a wide variety of problems in different domains
[24], and it has great potential for further improvement.

###  3.2 Privacy

Preserving privacy is an essential constraint when learning from sensitive
data that requires protection against data leakage and adversarial attacks. In
cybersecurity, the adversary is defined as a person or a group that performs
malicious actions to disrupt or corrupt a cyber system. Mothukuri et al. [25]
identified the different types of adversaries in the federated learning
framework and highlighted the solutions to improve systems vulnerability.
Table 1 describes the two types of adversaries caused by adversaries.

Table 1: Types of Adversaries in Cybersecurity Adversaries |  Action |  Aim |  Protocol
---|---|---|---
Semi-honest |  Passive |  Learning about the system |  ✓
Malicious |  Active |  Manipulation, corruption, control the system |  ✗

To address the problem of adversaries, Secure Multiparty Computation(SMC) is
developed. SMC is a framework that enables multiple parties to securely
process data while ensuring that no valuable information is leaked. This
scheme allows machine learning models to train sensitive data when most of the
parties involved are honest. The components are input parties, computation
parties, and result parties. For example, SMC is used as a privacy-preserving
method in studies on genome data [26], where the biobanks act as the input
parties that hold gene data and medical diagnoses information. The neural
hosts and other biobanks are chosen as computation parties, and designated
recipients are selected as result parties. This method allows us to combine
datasets from genome data collected from different individuals without
compromising their privacy. There are different protocols for implementing an
SMC that differ in size and number of supported input parties. In highly
regulated industries such as healthcare, more advanced privacy-preserving
methods are needed to facilitate the use of effective machine learning models
in the Federated learning framework [27]. The privacy-preserving methods are
categorized into the two following approaches [28]:

#### Perturbation Approach

This is a privacy-preserving approach based on adding noise to the data[29]
such as Differential Privacy, which is a perturbation mechanism that involves
adding noise to the data to obscure sensitive data and ensuring security in
the federated learning framework. Perturbation methods are effective in
securing the data, yet decrease the learning accuracy [30]. Differential
privacy is based on the concept of semantic security, which means the
encryption systems prevent enclosing any amount of information through the
learning process. In Differential Privacy, the outcome is assumed to be the
same regardless of the data if the model is implemented on two neighboring
databases. Two datasets of D and D’ are considered neighboring if they differ
at most in one random variable, and they are written as D$\backsim$D’. This
ensures that the algorithm’s output does not reveal any information about data
sharing and analysis. This feature is also known as $\epsilon$-$DP$. To
satisfy Differential Privacy, the Laplace or Gaussian mechanism is used for
data with integer or real-valued outputs, and noise is sampled from a Laplace
or Gaussian distribution to ensure $\epsilon$-DP for noisy data. The
exponential mechanism is used for categorical data, in which each output is
associated with a non-zero value for the probability of being selected based
on a utility function [31]. First, we compute the sensitivity of the utility
function and then compute the quality score of each output in the database.
The output is selected probabilistically. Tuning $\epsilon$ in the
Differentially Private mechanisms to ensure semantic privacy is one of the
challenges of using Differential Privacy for data privacy. Wei et al. [32]
demonstrate the trade-off between model convergence and privacy level. They
use a client scheduling strategy to improve model convergence while
maintaining privacy. Accumulation of noise can jeopardize the accuracy of
results if it exceeds a threshold on several operations, and the threshold is
on the depth of the operations rather than the number of operations performed
to infuse noise to encrypt the data. The depth of the operations is the
maximum degree of the evaluated polynomial. The operation depth is determined
by the privacy protection scheme as well as the level of speed and security.

#### Cryptography Approach

This approach preserves data privacy using cryptographic primitives and
includes homomorphic encryption, garbled circuits, secure processors, and
order-preserving encryption. In this section, we will look closely at
Homomorphic Encryption, which preserves privacy and accuracy for the cost of
higher running time. The components of Homomorphic Encryption are key
generation, encryption, decryption, and evaluation algorithm [33]. Homomorphic
Encryption is a cryptosystem that involves ciphering data using a public or
private key and sharing the key among peers to decipher the ciphertext. Data
is ciphered by mathematically transforming the data using addition and
multiplication operators [34]. Variations of Homomorphic Encryption ensure
data security among data centers and the central server. Qin et al. [35] use
Homomorphic Encryption for cloud-based privacy-preserving image processing,
including feature detection, digital watermarking, and content-based image
search. Limitations of Homomorphic Encryption are that the target space is
limited to 0,1 binary values, which is not a feasible representative in
practice. There are solutions to address this issue that expand the message
space to integers. However, in statistical learning, the values are not
limited to binary and integer values. Also, The encrypted ciphertext increases
drastically in size, sometimes by several orders of magnitude. This requires
additional storage and computation power since the learning procedure is more
computationally complex. Current Homomorphic Encryption schemes use only
addition and multiplication operators. Therefore, comparison tasks are not
supported. Improving encryption schemes to support subtraction and comparison
operations, such as inequalities, is an open research question in Homomorphic
Encryption.

###  3.3 Communication

Communication between local data centers and the central server requires
sufficient connectivity and bandwidth to ensure secure and private
communication between the entities and the entire system. While communication
efficiency is highly dependent on the existing infrastructure, reducing the
number of interactions between the data centers and the central server can
improve the efficiency of the federated learning model. For example, Shen et
al. [36] built a blockchain-based model for secure data sharing and training
using privacy-preserving Support Vector Machines. Their proposed model
requires only two interactions in each iteration, which provides higher
performance accuracy and data privacy with less computation cost. In addition,
the issue of fairness between the data centers and the central server arises
with the federated learning framework. The limitations posed by communication
efficiency and security are an ongoing challenge in implementing Federated
learning at scale [37].

###  3.4 Federated Aggregation

Secure aggregation is the function that receives model parameters from local
data centers and outputs the aggregated model parameters to update the
training model. Numerous studies explore aggregation methods to improve
learning accuracy in encrypted data. Lia and Togan [38] implemented federated
learning with secure aggregation in Python. The authors ensure privacy by
using SMC. Federated Averaging(FedAvg) is the baseline aggregating method in
federated learning. In this scheme, an initial global model is used to locally
train the datasets located on a network of distributed data centers. The
encrypted model parameters are uploaded to the central server, and the average
updates of local models are used to update and improve the global model. The
model parameters provided by the clients are aggregated at the central server
using Equation 2

$\delta_{t+1}\leftarrow\sum_{k=1}^{K}\frac{n_{k}}{n}\delta^{k}_{t+1}$ (2)

Then, the new model is sent back to the clients. This iterative process
continues until the model parameters converge to a specific performance level
or the task is completed. FedAvg is a practical approach since it does not
require data centers to disclose their data; thus, the models can be trained
locally. However, the communication cost can be high. To address this issue,
Li et al. [39] explore an adaptive communication frequency aggregation method
that helps the algorithm converge faster and have a smaller loss. They also
used a gradient sparse approach to reduce communication costs by decreasing
the parameters that need to be updated. Another variation of FedAvg is a
weighted FedAvg method, which has been proposed and has shown promising
performance in experiments on fault diagnosis [40]. Also, Hong et al. [41]
used weighted FedAvg for non-IID imbalanced data and evaluated the model on
CIFAR-10 and SVHN benchmark datasets. Co-operative aggregation is another
aggregation method (CO-OP) in which the local models are merged into the
global model by using a weighted scheme based on the local models’ age to
anticipate the time difference between them and how they have improved at each
iteration. In recent work, Chen et al. [42] utilized a discrepancy-based
weighted federated averaging method to address the inconsistencies in the
contributions of data centers in the global model for federated averaging. The
experiments show the effectiveness of federated transfer learning with the
proposed averaging method for fault diagnosis.

###  3.5 Privacy-preserving Machine Learning

In federated learning, a learning model is built and tuned collaboratively
between the central server and data centers [43]. Chandiramani et al. [44]
compared the efficiency of numerous machine learning models in the federated
learning framework on the benchmark fashion-MNIST data. Learning from
distributed data poses different issues and challenges. Therefore, different
machine learning models have been explored to compare the performance and
efficiency of the learning models. For example, to increase the security in
Android devices, Galvez et al. [45] built a federated learning malware
classification model using K-Nearest Neighbor, Logistic Regression, Random
Forest, and Support Vector Machines as machine learning models.

Figure 5: Federated Machine Learning Algorithms

Figure 5 provides a quick overview of machine learning models used in
federated learning literature.
In this section, we have provided a detailed survey of the traditional machine
learning algorithms and more recent learning schemes in this domain.

#### Regression Models

Regression is a predictive modeling approach for identifying the linear and
nonlinear relationships between independent variables and the target. Logistic
regression has been used in the framework of federated learning for different
applications. Yang et al. [46] explore a logistic regression model on clients’
credit card and healthcare data. Guo et al. [47] use a logistic regression
model to classify illness/health in the cloud environment named POMP. A
preprocessing technique and a Bloom filter are also used to reduce the
computational complexity in the pre-diagnosis process. The model is
implemented using Java and JPBC library. Dunner et al. [48] implement a ridge
regression model on five benchmark datasets to compare the performance of two
on Spark and Open MPI, two distributed machine learning frameworks, and
suggest recommendations on improving the models implemented in Spark. The
results from this paper show that fine-tuning the parameters in a distributed
machine learning model to adapt the system’s specifications and offloading the
Spark language-dependent overheads using C++ can improve computation
efficiency. The model is trained for when at least one of the data centers is
honest or honest but curious using SPINDLE, an operational system for
generalized linear models in distributed learning.
Regression models offer numerous benefits, such as privacy preservation,
continuous learning for real-time data analysis, and resource efficiency.
However, they deal with the challenges related to heterogeneous data, model
aggregation, and privacy concerns, common in many machine learning problems.

#### Support Vector Machines

Support Vector Machines are widely used in medical diagnosis, spam detection,
facial recognition, and analyzing financial data [49]. Bost et al. [50] use
Support Vector Machines to build efficient privacy-preserving algorithms and
evaluate the results on breast cancer diagnosis, credit card approval,
audiology, and nursery data. Xu et al. [51] implement linear and nonlinear
Support Vector Machines on horizontally partitioned data using the MapReduce
framework to preserve privacy. Saerom et al. [52] propose a Homomorphic
Encryption-friendly least-squares Support Vector Machines to train toy and
real-world datasets that outperformed the logistic regression model. Senekane
[53] proposed a privacy-preserving Support Vector Machines framework for image
classification. Liang et al. [54] focus on an outsourcing scheme for Support
Vector Machines classification with an efficient cryptographic primitive named
order-preserving encryption. Chanyaswad et al. [55] proposed a multi-kernel
method using the lossy-encoding scheme to protect the privacy of the data. The
training models are Support Vector Machines with an RBF kernel with multiple
gamma values and a Signal-to-Noise Ratio-based Support Vector Machines, which
is a Signal-to-Noise Ratio for kernel weight design that uses different
kernels. The kernel functions are linear, polynomial, Radial Basic
Function(RBF), Laplacian, and sigmoid. In terms of privacy, compressing single
kernels to form a multi-kernel provides effective results and maximizes
utility. The method based on the Signal-to-Noise Ratio method improves the
performance compared to uniform and alignment-based methods. Hsu et al. [56]
design a privacy-preserving system for malware software detection using SGD-
based Support Vector Machines and SMC techniques. The paper by Zhang et al.
[57] proposes a solution for the problem of human motion recognition in
multimedia interaction scenarios in a virtual reality environment using
Support Vector Machines. Chen et al. [58] focus on the issue of computation
efficiency and low latency of edge computing for augmented reality
applications. Hartmann et al. [59] proposed a Support Vector Machines model in
a privacy-preserving setting, called secret Support Vector Machines, for
predicting user gender from tweets based on the online and offline evaluation.
Lu et al. [60] propose a privacy-preserving feature selection using a multi-
class Support Vector Machines named PPM2C, which is evaluated with PAN-SVM and
LIB-SVM. The results from this study show that multi-class Support Vector
Machines (PPM2C) reduce the chances of overfitting compared to regular Support
Vector Machines. Despite being an effective learning method, implementing
privacy-preserving Support Vector Machines on data with missing values poses
numerous challenges that must be addressed. Omer et al. [61] built a
distributed Support Vector Machines model with multiple imputations by chained
equations on vertically partitioned data. In this work, the privacy of the
data is ensured with the Paillier cryptosystem. The evaluation of the proposed
scheme shows higher accuracy and lower computation time compared to the
centralized model on imputed data. Medical diagnosis systems can significantly
benefit from advances in federated learning. Machine learning methods were
used to design a secure framework to prevent severe health conditions by
diagnosing patients based on their symptoms and the data collected from
wearable monitoring devices that monitor heart rate, temperature, oxygen
saturation, and other vital signs, and voice-controlled devices such as Google
Assistant, Amazon Echo, and Apple Siri [62]. Privacy-preserving Support Vector
Machines models have been particularly successful in healthcare applications.
Wang et al. [63] focus on outsourced Support Vector Machines and EPoSVM
(Efficient and Privacy-preserving Outsourced Support Vector Machines) for data
classification in the Internet of Medical Things, which results in an
improvement in learning accuracy and security compared to Support Vector
Machines. Zhu et al. [64] explored an efficient and privacy-preserving online
medical pre-diagnosis framework (eDiag) using nonlinear kernel Support Vector
Machines. Ahmed et al. [65] developed ”mLung”, a cloud-based privacy-
preserving service to detect chronic pulmonary issues from lung sounds such as
cough. The analysis is performed on a personal mobile phone to ensure privacy.
Medical components of the drugs must be kept private by pharmaceutical
companies. Wang et al. [66] explored an encrypted kernel Support Vector
Machines using Homomorphic Encryption. They have built a sub-image to position
different sections of the image so that a face can appear. The authors trained
and tested their model on the BioID Face Database.
One of the key benefits of Support Vector Machines in federated learning is
their ability to generalize well from limited data, making them suitable for
scenarios where each participant has a relatively small dataset. However, some
of the challenges and limitations of Support Vector Machines are:

  * •

Complexity of kernel functions: Support Vector Machines rely on kernel
functions to handle the data in nonlinear space. However, selecting
appropriate kernel functions in a framework with diverse data sources can be
challenging, as different participants may require different kernel types.

  * •

Communication overhead: Support Vector Machines are computationally expensive
due to dealing with large datasets and complex kernel functions, making them
unsuitable for edge devices or environments with limited computational power.

  * •

Hyperparameter tuning: Support Vector Machines have multiple hyperparameters,
such as the regularization parameter (C) and the kernel parameters.
Hyperparameter tuning across multiple participants in a federated setting can
be complex and time-consuming.

While Support Vector Machines can be computationally expensive, they offer
strong generalizations that can lead to robust models even with limited local
data.

#### Tree Models

Decision Tree and Random Forest are a group of effective and widely used
models for data classification and regression [67]. Khodaparast et al. [68]
presented a Decision Tree algorithm equipped with a federated learning
framework for horizontal and vertically partitioned data. Yadav et al. [69]
presented a new Decision Tree-based model in a privacy-preserving manner in
which the data are partitioned vertically into multiple parties, and the
parameters are sent to the central server. Badsha et al. [70] present a
privacy-preserving Decision Tree framework to build and learn the tree-based
model without requiring the parties to disclose private information. The
authors use Homomorphic Encryption to maintain privacy, while the parties are
assumed to be honest but curious. The Gini Index is used to measure the
classification capability of the model. Canillas et al. [71] explored a
privacy-preserving Decision Tree framework for private fraud detection systems
at SiS ID, a French business platform. The model is used to classify
transactions into four risk classes. The model’s accuracy depends on the
configuration of the encryption key and the number of nodes for the Decision
Tree. The proposed model utilizes a Decision Tree algorithm that helps improve
the diagnosis pace and accuracy based on the patient’s symptoms without
disclosing the patient’s private data. Xue et al. [72] propose a consent-based
privacy-preserving Decision Tree model for the evaluation scheme. The additive
Homomorphic Encryption method and a secure comparison model are used. Also,
Xue et al. [73] propose a privacy-preserving Decision Tree for classification
using additive Homomorphic Encryption, which provides lower computation and
communication overhead. Hou et al. [74] explore Random Forest with a Decision
Tree for data classification and study the impact of tree depths in Decision
Tree on privacy and classification. A privacy budget is allocated for nodes at
different depths in the Decision Tree. Guan et al. [75] explore a budget
allocation mechanism for Decision Tree construction for balancing the
excessive noise introduced at leaf nodes. The iterative process speeds up the
selective aggregation process. The tree is constructed based on the C4.5
method. Lv et al. [76] proposed a hybrid Decision Tree algorithm for
constructing a Random Forest to balance privacy and classification accuracy.
In the paper by Xin et al. [77], the authors propose a new differentially
private greedy Decision Tree algorithm called (DPGDF) which is a combination
of greedy trees and parallel combination theory. Zhao et al. [78] explore a
tree-based data mining model for regression and binary classification tasks.
In this work, a privacy-preserving Gradient Boosting Decision Tree (GBDT)
model is aggregated into an ensemble. Random Forest is also used for feature
engineering. Fritchman et al. [79] propose a tree ensemble approach to learn
from the data collected in healthcare institutes securely. Zhang et al. [80]
has built a secure Parkinson’s diagnosis framework using non-speech body
sounds such as breathing and coughing.
The main advantages of tree-based models, including Decision Trees and Random
Forests, are their interpretability, ensemble learning for robustness, and
feature importance analysis.

#### Naïve Bayesian Algorithms

Naïve Bayesian algorithms is a supervised learning algorithm centered around
the Bayes theorem, which is based on the assumption that there is conditional
independence among each pair of features if the class is known. In smart
environments such as smart cities, data privacy is crucial. Amma and
Dhanaseelan [81] explore a Naïve Bayesian classification framework for
privacy-preserving machine learning on the cloud using smart city data. The
authors validated the results using Viz road traffic, pollution, and parking
data collected from the City Pulse Smart City dataset. Part et al. [82]
introduced a novel federated learning architecture that consists of three
layers. In the edge layer, the data is processed, the machine learning models
are trained in the fog layer, and the results are aggregated in the
centralized cloud layer. Data partitioning poses specific challenges when
learning from the data in a distributed setting. Vaidya et al. [83]
investigate a privacy-preserving Naïve Bayesian classification model and
compare the results on horizontal and vertically partitioned data. Yurochkin
et al. [84] propose a Bayesian non-parametric federated learning framework
with neural networks. The model is evaluated on two benchmark data sets for
image classification. The paper by García-Recuero [85] addresses the issue of
detecting and discouraging abusive behavior in online social networking
applications such as Twitter by limiting the accessibility to user-sensitive
information. This work uses feature engineering on relative importance
calculated from the Random Forest learning algorithm. Li et al. [86] use Naïve
Bayesian and a hyperplane-based decision model for classification. Furthering
this work, Chai et al. [87] propose an outsourced encryption protocol to
improve the security vulnerability of Li’s model. The classification models
are trained using Scikit-learn. Paillier cryptosystems are used in this work
due to light operations. In a paper by Yang et al. [88], the authors propose a
communication-efficient privacy-preserving framework based on the Naïve
Bayesian method to predict the disease risk for e-health applications.
Sharkala et al. [89] introduce a privacy-preserving machine learning algorithm
for horizontally and vertically partitioned data based on a tree augmented
Naïve Bayesian classifier. A third party conducts the operations, and the data
is encrypted. Teo et al. [90] propose a privacy-preserving algorithm using
kernel regression and Naïve Bayesian classifier for multiparty computation,
and the Paillier cryptosystem is used for encryption. Medical diagnosis
systems are the main application of privacy-preserving machine learning. Liu
et al. [91] build a secure diagnosis scheme using a Naïve Bayesian classifier.
Furthermore, malware detection systems protect the user’s identity by
identifying malware API call fragments. Lin et al. [92] propose a privacy-
preserving Naïve Bayesian model for malware detection. Talbi et al. [93]
compare their classification algorithms of Naïve Bayesian, Decision Tree, and
logistic regression on encrypted data.
Naive Bayesian algorithms provide probabilistic predictions and can quantify
uncertainty. In federated settings, this is a significant advantage for risk
assessment and decision-making, particularly in applications such as crisis
management and disaster response scenarios where uncertainty plays a
significant role.

#### Deep Learning

Deep learning has been the dominant learning method from structured and
unstructured data in recent years. Deep learning is the burgeoning powerful
technique in the field of machine learning. Deep architectures are useful for
learning complicated patterns in large-scale data, attracting much attention
in academia and industry. Different topologies and architectures with real-
world applications exist. These models have been used in different areas such
as Computer Vision, Natural Language Processing, and speech recognition [94,
95]. The improvements in implementing a secure Neural Network model on the
cloud create a platform for scalable Neural Networks to be used as a service
[96, 97]. A Recurrent Neural Network is a variation of a forwarding
propagation Neural Network in which the neurons in the hidden layers receive
the input value with a delay in time and access information from previous
iterations in the current layer. Recurrent Neural Network is useful in Natural
Language Processing, where knowledge about the previous words in a sentence is
necessary for predicting the next word. Text mining and Natural Language
Processing, which is the process of extracting knowledge from text documents,
are used for learning from data collected from highly sensitive resources such
as homeland security for crime fight and detecting terrorism activities.
Therefore, building secure federated learning text analysis methods is
necessary to ensure no sensitive information is disclosed. To this end,
Costantino et al. [98] propose using an out-of-bag classification method to
detect terrorist activities on Twitter. Convolutional Neural Network is a deep
learning architecture that has gained popularity in Computer Vision [99]. This
architecture uses one or more convolutional layers to extract high-level
features. Wang et al. [100] investigate a privacy-preserving Natural Language
Model framework to compute word representations using deep learning. Abadi et
al. [101] explore training a Neural Network model with a non-convex objective
function, implemented using TensorFlow in Python, with differentially private
Stochastic Gradient Descent method, the moments’ accountant, and
hyperparameter tuning. The security protocol is a semi-honest security model
that is a secure computation method based on additive secret-sharing
techniques (secure addition, subtraction, and secure multiplication). Xia et
al. [102] designed a Graph Convolutional Network model for predicting traffic
flow quickly and efficiently. Deep learning methods can also be combined with
other classifiers, such as linear Support Vector Machines, for the
classification of images. Niu et al. [103] explore a deep learning framework
for mobile sensing systems. Lin et al.[104] propose a Recurrent Neural Network
framework called the Predictive Clinical Decision (PCD) scheme, which is used
for e-health applications. Eye-tracking devices are the main technology in
virtual reality and augmented reality that can improve efficiency through
gaze-based optimization methods. The eye-wear and eye-tracking devices used in
the auto industry can pose privacy issues for the driver and bystanders [105].
Therefore, Steil et al. [106] explore a privacy-preserving method for a first-
person video dataset of daily life recordings. The authors propose the
PrivacEye method that combines Computer Vision with eye movement analysis
techniques. Another privacy concern with Computer Vision technologies in
facial recognition systems such as Google Street View is when personal images
of individuals are shared via different data centers.
Deep learning models have demonstrated great potential for highly accurate and
competitive results when dealing with diverse and large datasets. While
increasing model complexity in neural network architectures helps improve
generalization, excess complexity results in overfitting and computation
overhead. Adaptation, optimization, and the integration of privacy-preserving
techniques are essential to harness the strengths of deep learning while
mitigating the specific challenges and limitations present in federated
settings.

#### Unsupervised Machine Learning

Current Federated learning machine learning-based models are constructed based
on supervised learning. However, in most applications, no or little labeled
data exists. Thus, it is appropriate to use unsupervised learning methods.
While there has been considerable progress on federated transfer learning to
cope with data with few labels, applying unsupervised learning in a federated
setting remains a bottleneck for many applications. Clustering techniques have
been employed to deal with the challenges of unlabeled data. While K-means
clustering is widely used for pattern recognition in gene detection and image
segmentation, a modified framework is required when the data is sensitive. Zhu
and Li [107] proposed a secure aggregation and division protocol based on
Homomorphic Encryption to build a secure clustering algorithm. Al-Saeidi et
al. [108] proposed a clustering analysis for improving the communication cost
in federated learning using the human activity recognition dataset. A secure
weighted average protocol and secure number comparison protocol are used for
privacy-preserving. Five different classification algorithms were explored:
multi-layer perceptron, K Nearest Neighbor, Sequential Minimal Optimization,
Naïve Bayesian, and J48 (an implementation of Decision Tree classification in
WEKA). Anikin and Gazimov [109] proposed a clustering algorithm named Density-
Based Spatial Clustering of Applications with NOISE (DBSCAN) for vertically
partitioned data. Romsaiyud et al. [110] investigate a privacy-preserving K
Nearest Neighbor model for pattern recognition, with automated hyperparameter
tuning to improve model accuracy and a cryptographic hash function to ensure
data security.
The benefits of unsupervised learning techniques in federated learning are:

  * •

Privacy-preserving clustering: Clustering algorithms can perform data analysis
without the need for explicit labels or excess information sharing, which is
beneficial when dealing with sensitive data.

  * •

Data exploration and anomaly detection: Unsupervised models excel at data
exploration, allowing practitioners to identify the underlying patterns,
anomalies, and outliers within local datasets. This exploratory capability is
valuable for uncovering insights without exposing private data.

  * •

Reduced labeling effort: Limited labeled data is a well-known issue in machine
learning. Unsupervised learning models can reduce the labeling effort by
enabling semi-supervised or self-supervised learning approaches without
exposing the data.

With appropriate evaluation strategies, we can effectively leverage the
strengths of unsupervised learning while mitigating the specific challenges
and limitations of federated learning.

#### Ensemble Learning

Ensemble learning is a general approach that seeks to improve learning
performance by aggregating the results from multiple classifiers. The paper
published by Attota et al. [111] used an ensemble multi-view federated
learning model to identify intrusion in IoT devices to improve model
efficiency against different attacks. Ma et al. [112] apply edge computing
methods for medical diagnosis using the XGBoost model. They use a lightweight,
adaptive boosting classification method (AdaBoost) for facial recognition on
FERET, a standard face recognition evaluation database. The data is encrypted
when sent to two servers for distributed learning.
Ensemble learning is an effective approach in the machine learning domain and
has yet to be extensively explored in the federated learning framework. There
is a very limited number of papers published in peer-reviewed journals using
this approach. However, they demonstrate the potential of ensemble learning in
federated learning.

#### Meta-heuristic Approaches

Apart from the more commonly used machine learning algorithms mentioned above,
meta-heuristic approaches have also been introduced to the federated learning
domain. Polap and Wozniak [113] used a novel approach based on parallelism to
improve classification models’ efficiency in federated learning. The authors
demonstrate the effectiveness of their approach when the sample size is
relatively small. In another paper, [114], they explore using a meta-heuristic
federated learning framework for image classification in the presence of
poisoning attacks. With application in IoT and smart city services, Qolomany
et al. [115] investigate using Particle Swarm Optimization for efficiently
tuning the hyperparameters in the machine learning model. Utilizing meta-
heuristic approaches can be further explored as a novel approach to improve
the efficiency of the training model in federated learning.
Metaheuristic algorithms are versatile and adaptable to various problem
domains. They can be customized to suit the specific requirements and
constraints of federated learning scenarios.

#### Blockchain Technology

Despite improvements in Homomorphic Encryption and Differential Privacy in
preserving privacy, there is always a trade-off between learning accuracy and
privacy. To overcome such issues, federated learning can be equipped with
blockchain technology [116, 117]. Utilizing blockchain technology in federated
learning is an emerging field in federated learning and decentralized data
storage and processing [118, 119]. Nguyen et al. [120] survey the advances and
challenges of federated learning with blockchain technology. In edge computing
and learning from IoT data, blockchain federated learning is a solution to
issues in data storage, communication cost, and privacy of sensitive data.
Wang et al. [121, 122] use a blockchain-distributed setting as the groundwork
for federated learning to ensure additional privacy protection from servers
and prevent malicious attach on user-sensitive data. Kang et al. [123]
proposed a federated learning framework based on a blockchain mechanism and
introduced reputation as a metric to identify reliable data and propose a
reliable framework to learn from the data on mobile networks. Later, Kang et
al. [124] used multiple blockchains to design a cross-chain framework to
improve the scalability and communication efficiency of federated learning for
training the data on IoT devices. An example of other applications is a
classification of COVID-19 cases from multiple resources using blockchain-
based federated learning. Comparing the federated learning framework to
centralized models shows improvement in diagnosing COVID-19 patients [125].
Leveraging blockchain technology within the context of federated learning
introduces many benefits, such as:

  * •

Data privacy and security: Blockchain technology has inherent privacy
protection capabilities allowing participants to maintain control over their
data while securely contributing to the global model.

  * •

Transparent and trustworthy transactions: The decentralized nature of
blockchain ensures that all transactions and updates are transparent and
traceable. This feature mitigates concerns of data tampering and adversarial
attacks.

  * •

Smart contracts for governance: Smart contracts are programmable scripts
executed on the blockchain that can be employed for governing federated
learning agreements and model updates. This automation streamlines the process
and enforces predefined rules and policies, reducing the risk of malfunction
and misuse.

Despite its benefits, implementing a blockchain-based federated learning
system is complex and requires expertise in both blockchain technology and
machine learning. Developing and maintaining such a system is challenging as
some blockchain networks consume a significant amount of energy. This
environmental impact may not align with sustainability goals in federated
learning.

#### Reinforcement Learning

Incorporating other learning approaches into federated learning has shown
promising results in different applications [126]. Liu et al. [127] built a
reinforcement learning framework, which is a learning system through trial-
and-error interactions between agents and environments combined with cloud
computing and IoT technology to create a dynamic system for cancer patient
treatment regimes. Wang et al. [128] proposed a reinforcement learning
mechanism to introduce a rewards system that optimizes accuracy and
communication efficiency. A reinforcement learning approach is also used for
evaluating node contributions and improving the pricing strategy in federated
learning for IoT devices [129]. Krouka et al. [130] investigate different
aggregation schemes in a reinforcement learning-federated learning framework
to improve communication costs.
The benefits of combining reinforcement learning and federated learning are :

  * •

Dynamic model adaptation: Reinforcement Learning models can adapt dynamically
to changing data distributions and evolving environments. In federated
learning, where data sources may drift or have different characteristics,
Reinforcement Learning can facilitate model adjustments for improved
performance.

  * •

Sequential decision-making: Reinforcement Learning is well-suited for
sequential decision-making tasks. In federated learning scenarios, this
capability can be valuable for applications that involve sequential
interactions or recommendations.

Reinforcement Learning models often require extensive training and exploration
of different policies, which can be computationally expensive and time-
consuming. Reinforcement Learning must efficiently find the optimal policy by
balancing the exploration of new actions and the exploitation of known
policies. Managing the trade-off between exploration and exploitation is
necessary to avoid excessive data sharing or overfitting.

##  4 Challenges and Open Problems

Federated learning is at the interface of several research areas, such as
optimization, distributed learning, cryptography, and communication theory. In
the last few years, many algorithmic developments have been accomplished with
a focus on theory and applications. However, there are still several
challenges and open problems in federated learning. In a recent paper, some
advances and open problems are discussed [131]. In addition, central to the
future of federated learning are the data-driven challenges outlined in Table
2.

Table 2: Data-driven Issues in Federated Learning Issues |  |  Challenges |
---|---|---|---
Unlabeled Data |  |  Labeling the data require considerable time and resources. |
Non-IID Data |  |  Different distributions in data centers negatively affect learning performance and efficiency. |
Imbalanced Data |  |  imbalanced Data result in reduced learning performance in the minority class. |

The data-driven issues pose various challenges for efficient and accurate
training of federated learning models.

  * •

Unlabeled data: The lack of annotated data with good quality is one of the
limitations in this setting. When the data is unlabeled, and labeling the data
is either impossible or too cost-inefficient, it is important to modify the
machine learning algorithms to be able to learn from partially annotated data
efficiently.

  * •

Non-Independent and Identically Distributed (Non-IID) Data: Non-IID data in
federated learning refer to differences in the distribution of the available
data over the data centers. It is also possible that the data become locally
non-IID over time, which requires modifying existing algorithms or developing
new ones. To address this issue, Sattler et al. [132] proposed a compression
network to improve the communication efficiency and robustness of Federated
learning on non-IID data. Ma et al. [133] and Zhu et al. [134] investigated
the recent advances in solving non-IID data in federated learning and the
future trends in research on this issue. They also compared different model
architectures of deep learning used in the literature on federated learning.

  * •

Imbalanced data: The data is imbalanced when the number of instances in one
class is significantly larger than in the other. This issue can arise in the
taxonomy of the data centers or the overall data. The challenges posed by
data-driven issues require experiments on achieving highly efficient and
accurate learning systems.

Other challenges that need to be overcome are:

  * •

Fairness: The issue of fairness in federated learning occurs at different
levels, such as fairness in communication capacities, machine learning models,
and aggregation results. To this end, fairness metrics must be defined to
evaluate the model from privacy, accuracy, and fairness perspectives [135].
Also, Lyu et al. [136] propose the concept of collaborative federated
learning, which ensures fairness in how the clients impact the global model in
the aggregation process. Despite the improvements, there is a lack of an
integrated approach that ensures fairness in different aspects of federated
learning.

  * •

Scalability: To implement a federated learning protocol at scale, it must
avoid the curse of dimensionality when data is large. To tackle the challenge
of dimensionality, Principal Component Analysis has been employed in
unsupervised settings such as the work by Al-Rubaie [137], which developed a
privacy-preserving Principal Component Analysis to reduce the dimensionality
of the horizontally partitioned data. In addition, other methods, such as
Discriminant Component Analysis, can be explored for efficient feature
engineering and dimensionality reduction to improve accuracy and computation
cost and uphold privacy.

Possible future research directions are:

  * •

Game theory: Recent connections between game theory and control can provide
new insights into federated learning and new algorithmic developments. An
attempt along these lines is the paper of Mehrjou[138], which connects
federated learning with mean field games by presenting federated learning as a
differential game and discussing the properties of the equilibrium of this
game.

  * •

Quantum optimization: Another future research area is quantum optimization
applied to federated learning. Distributed learning across several quantum
computers could significantly improve the training time and potentially
improve data privacy [139]. Connections with multi-objective optimization,
such as [140], can benefit algorithmic developments.

  * •

Multiple Kernel Learning: Exploring the connections between federated learning
and multiple kernel learning also holds potential for advancing algorithmic
solutions.

Furthermore, federated learning must navigate complex challenges, including
exploring more advanced ways of combining local networks, using different
machine learning and ensemble models in addition to preprocessing techniques,
performing experiments on larger datasets and further enhancement efficiency,
and designing accurate and reliable machine learning models suitable for GPU
implementation. In summary, federated learning stands as a field where
innovation and collaboration are essential. By collectively addressing these
challenges and pursuing interdisciplinary connections, The research community
can integrate federated learning into a future where it serves as a
cornerstone for secure, efficient, and privacy-conscious machine learning
applications.

##  5 Conclusion

We have conducted an extensive literature review of federated learning from
the machine learning point of view, complementing other recent literature
reviews. This review will be useful for researchers in academia and industry
and possibly a useful tool for graduate students who want to work in this
area. Federated learning was proposed as a solution to the issue of data
leakage and loss of privacy in machine learning. With a large amount of data
at hand, there is a burgeoning demand for federated learning as potentially
being the solution to private and environmental-friendly machine learning at
scale. Decentralized learning strategy and privacy protection mechanisms in
federated learning grant us access to otherwise unavailable data. Hence, we
can expand machine learning in domains such as IoT and healthcare and crisis
management in natural and human-caused disasters that require privacy
preservation. In recent years, there has been an increasing number of papers
published in this domain, and the goal of this study is to provide an overview
of federated learning and the existing privacy-preserving machine learning
algorithms used in this framework, in addition to their potential and
limitations in various applications. Despite our effort to thoroughly search
the literature on federated learning, we limited our search to published
papers in peer-reviewed journals in English. Therefore, other novel approaches
might be found in the papers not included in this survey.

## Declarations

Conflict of interest The authors declare no competing interests

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