mobile networks. Inline with this goal, the energy consumption of FL during
the training process [313] must be carefully optimized and underlying schemes
must be energy-aware designed. It is understood, FL is more energy-efficient
than traditional centralized learning approaches, but there are still several
energy wasting operations to be rethought for green learning [314]. First, the
distributed nature of FL requires more computing resources, which may results
in increased energy consumption. Second, FL might require data to be
transferred across devices/edge/cloud, leading to increased energy
consumption. Finally, the FL process typically requires multiple rounds of
data transmission and computation, which further increases the overall energy
consumption. In [315] a distributed FL framework is designed to overcome the
connectivity and energy depletion for distant devices. The framework is
suitable for mobile edge computing that connects the devices in a distributed
manner using clustering protocols. This approach resolves centralized systems
issues and exhibits scalable, faster, and energy-efficient communication when
exchanging the trained models.

In the near future, FL is trusted to run large neural networks or similar
algorithms at mobile devices and client handsets level. This is not feasible
for resource-constrained devices. In this regard, one major challenge is how
to design FL algorithms to minimize the use of computing and communication
resources. One potential perspective is to efficiently use quantization [316,
317], which was shown to significantly reduce energy consumption. Indeed,
there is a need for new frameworks to build green FL algorithms that achieve
both high computational and communication efficiency, while requiring low
resource and energy budget.

##  IX Conclusion

Federated learning shows great potential in addressing privacy concerns and
communication costs in various sectors, especially in current and upcoming
mobile network generations. This paper thoroughly examines FL’s role in
wireless communication networks, comparing different ML architectures and
exploring the FL fundamentals, techniques, and frameworks. We also review the
latest contributions that use FL to improve communication and KPIs within the
protocol stack. Finally, we discuss important insights and challenges that
come with deploying FL strategies in 5G, 6G, and beyond. Overall, FL is a
vital research domain with far-reaching implications for the future, and this
paper provides a detailed analysis of its potential and impact.

## Table of Abbreviations & Acronyms

The main abbreviations used in this survey are listed bellow:

Abbreviation | Description
---|---
5G | Fifth Generation Mobile Network
6G | Sixth Generation Mobile Network
NR | (5G) New Radio
RF | Radio Frequency
AI | Artificial Intelligence
FL | Federated Learning
ML | Machine Learning
DL | Deep Learning
FRL | Federated Reinforcement Learning
OSI | Open Systems Interconnection
PHY | Physical Layer
NET | Network Layer
MAC | Medium Access Control Layer
APP | Application
IoT | Internet of Things
CNN | Convolutional Neural Network
NN | Neural Network
HFL | Horizontal Federated Learning
VFL | Vertical Federated Learning
FTL | Federated Transfer Learning
MMVFL | Multi-class Vertical Federated Learning
HFCL | Hybrid Federated Centralized Learning
FedAvg | Federated Averaging
SimFL | Similarity Federated Learning
TFF | TensorFlow Federated
FATE | Federated AI Technology Enabler
FedML | Federated Machine Learning
FedRec | FL-based receiver
AMC | Automatic Modulation Classification
TCP | Transmission Control Protocol
CSI | Channel State Information
RF | Radio Frequency
RIS | Reconfigurable Intelligent Surfaces
FED-WG | Federated Weights and Gradients
MIMO | Multiple Input Multiple Outputs
mm-Wave | millimeter Wave
SPIM | Spatial Path Index Modulation
NOMA | Non-orthogonal Multiple Access
MTD | Machine Type Devices
MEC | Multi-access edge computing
LSTM | Long Short Term Memory
SVM | Support Vector Machine
UAV | Unmanned Aerial Vehicle
QoE | Quality of Experience
HDRL | Hybrid FL Reinforcement Learning
VR | Virtual Reality
AR | Augmented Reality
XR | Extended Reality
TI | Tactile Internet
URLLC | Ultra-Reliable Low-Latency Communication
TC | Traffic Classification

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Maryam Ben Driss (Student Member, IEEE) received the B.Sc. degree in Computer
Science in 2018 and the M.Sc. degree in Big Data and Data Science in 2020 from
the University of Hassan II, Faculty of Sciences Ben M’Sik, Casablanca,
Morocco. She is currently pursuing her Ph.D. degree at the National Higher
School of Electricity and Mechanics (ENSEM) in Casablanca. Her research
interests include Artificial Intelligence, Federated Learning, Machine
Learning, Deep Learning, Cellular Networks, 5G, 6G and beyond.

Essaid Sabir (Senior Member, IEEE) received the Ph.D. degree (Hons.) in
networking and computer engineering from Avignon University, France, in 2010.
He has been a non-tenure-track Assistant Professor at Avignon University, from
2009 to 2012. He has been a Professor at Hassan II university of Casablanca
until late 2022, where he was leading the NEST research Group. He is a
professor with the department of computer science, Université du Québec à
Montréal. His research interests include 5G/6G, wireless networks, IoT, AI/ML,
and game theory. His work has been awarded in four international conferences.
To bridge the gap between academia and industry, he founded the International
Conference on Ubiquitous Networking (UNet) and co-founded the WINCOM
conference series. He serves as a guest editor for many journals. He organized
numerous events and played executive roles for other major events.

Halima Elbiaze (Senior Member, IEEE) received the B.S. degree in applied
mathematics from the University of MV, Morocco, in 1996, the M.Sc. degree in
telecommunication systems from the University of Versailles, in 1998, and the
Ph.D. degree in computer science from Institut National des
Télécommunications, Paris, France, in 2002. Since 2003, she has been with the
Department of Computer Science, Université du Québec à Montréal, QC, Canada,
where she is currently an Associate Professor. She has authored or co-authored
many journal and conference papers. Her research interests include network
performance evaluation, traffic engineering, and quality of service management
in optical and wireless networks.

Walid Saad (Fellow Member, IEEE) received his Ph.D. degree from the University
of Oslo in 2010. He is a Professor at the Department of Electrical and
Computer Engineering at Virginia Tech where he leads the Network Science,
Wireless, and Security (NEWS) laboratory. His research interests include
wireless networks, machine learning, game theory, cybersecurity, unmanned
aerial vehicles, semantic communications, and cyber-physical systems. He was
the author/co-author of eleven conferences’ best paper awards and the 2015 and
2022 IEEE ComSoc Fred W. Ellersick Prize.