Federated Learning for Wireless Communications: Motivation, Opportunities, and Challenges

By Solmaz Niknam, Harpreet S. Dhillon, J. H. Reed. IEEE Communications Magazine, 2019

742 citations recorded by the source corpus.

There is a growing interest in the wireless communications community to complement the traditional model-driven design approaches with data-driven machine learning (ML)-based solutions. While conventional ML approaches rely on the assumption of having the data and processing heads in a central entity, this is not always feasible in wireless communications applications because of the inaccessibility of private data and large communication overhead required to transmit raw data to central ML processors. As a result, decentralized ML approaches that keep the data where it is generated are much more appealing. Due to its privacy-preserving nature, federated learning is particularly relevant for many wireless applications, especially in the context of fifth generation (5G) networks. In this article, we provide an accessible introduction to the general idea of federated learning, discuss several possible applications in 5G networks, and describe key technical challenges and open problems for future research on federated learning in the context of wireless communications.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/275b313748602da081d1254ee7e6d5e4af33c826

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Citations in the corpus

6G Internet of Things: A Comprehensive Survey (2021, background): “SIoT platforms can enable energy sustainability via the use of aerial IoT devices such as UAVs and balloons, with renewable sources from the space that may be not available at the ground-based stations [115].”
Heterogeneous Federated Learning: State-of-the-art and Research Challenges (2023, background): “[165] They discuss the applications and challenges of federated learning in wireless communication environments.”
Federated Learning in Edge Computing: A Systematic Survey (2022, background): “The authors of [90] highlighted FL’s applications in wireless communications, specifically the cellular network architecture.”
A Comprehensive Survey of Privacy-preserving Federated Learning (2021, background): “None of these three surveys [84, 101, 204] analyzed the privacy issues related to FL. Niknam et al. [132] discussed FL applications in 5G networks and challenges of FL for wireless communications.”
Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges (2020, neutral): “10 [27], [124], [125].”
Holistic Network Virtualization and Pervasive Network Intelligence for 6G (2023, background): “Due to such decentralized model training, FL is capable of preserving privacy and can be applied in privacy-sensitive network management scenarios [137], [138].”
Reconfigurable Intelligent Surfaces: Principles and Opportunities (2020, background): “point in the area of large-scale machine learning and distributed optimization [145].”
Federated learning for 6G communications: Challenges, methods, and future directions (2020, background): “Moreover, due to large overhead caused by centralized data aggregation and processing, traditional centralized ML schemes might not be suitable for ubiquitous ML [5].”
Machine Learning in IoT Security: Current Solutions and Future Challenges (2019, background): “For DRL or Deep Q-Learning (DQL), the learning is a continuous process and the computational complexity depends on the specific algorithm used (for example, the most common algorithm used for DQL is “Experience Replay” algorithm and the complexity is a function of the “batch size” used for training.”
Federated Machine Learning: Survey, Multi-Level Classification, Desirable Criteria and Future Directions in Communication and Networking Systems (2021, background): “The potential of federated learning in the domains of wireless communication and mobile edge network has been studied in [14] and [15] respectively.”
Federated Learning in Smart City Sensing: Challenges and Opportunities (2020, positive): “Federated Learning deﬁnes a machine learning framework under a virtual model, which is designed to solve the problem of different data owners collaborating without exchanging data [122].”
Distributed Learning in Wireless Networks: Recent Progress and Future Challenges (2021, background): “In [8]–[12], the authors provided compre-”
Vertical Federated Learning: Concepts, Advances, and Challenges (2022, background): “[183] discussed the opportunities for VFL to be utilized in 5 G wireless networks.”
Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challenges (2021, background): “Section II presents the state-of-the-art and fundamentals of FL and blockchain.”
Federated Learning for Internet of Things: A Comprehensive Survey (2021, methodology): “An overview on the use of FL for wireless communications was provided in [19] where the roles of FL in 5G applications are discussed, such as edge computing, spectrum sharing, and 5G core network management.”
A Survey on Federated Learning: The Journey From Centralized to Distributed On-Site Learning and Beyond (2021, background): “[18] preserved the privacy of the data in wireless communication.”