Edge-native intelligence for 6G communications driven by federated learning: a survey of trends and challenges

Al-Quraan, M., Mohjazi, L. , Bariah, L., Centeno, A. , Zoha, A. , Arshad, K., Assaleh, K., Muhaidat, S., Debbah, M. and Imran, M. A. (2023) Edge-native intelligence for 6G communications driven by federated learning: a survey of trends and challenges. IEEE Transactions on Emerging Topics in Computational Intelligence, 7(3), pp. 957-979. (doi: 10.1109/TETCI.2023.3251404)

Abstract

New technological advancements in wireless networks have enlarged the number of connected devices. The unprecedented surge of data volume in wireless systems empowered by artificial intelligence (AI) opens up new horizons for providing ubiquitous data-driven intelligent services. Traditional cloud-centric machine learning (ML)-based services are implemented by centrally collecting datasets and training models. However, this conventional training technique encompasses two challenges: (i) high communication and energy cost and (ii) threatened data privacy. In this article, we introduce a comprehensive survey of the fundamentals and enabling technologies of federated learning (FL), a newly emerging technique coined to bring ML to the edge of wireless networks. Moreover, an extensive study is presented detailing various applications of FL in wireless networks and highlighting their challenges and limitations. The efficacy of FL is further explored with emerging prospective beyond fifth-generation (B5G) and sixth-generation (6G) communication systems. This survey aims to provide an overview of the state-of-the-art FL applications in key wireless technologies that will serve as a foundation to establish a firm understanding of the topic. Lastly, we offer a road forward for future research directions.

• Item Type: Articles
• Additional Information: Funding: Ajman University Internal Research Grant No. 2022-IRG-ENIT-18.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Centeno, Dr Anthony and Zoha, Dr Ahmed and Bariah, Dr Lina and Imran, Professor Muhammad and Alquraan, Mohammad Mahmoud Younes and Mohjazi, Dr Lina
• Authors: Al-Quraan, M., Mohjazi, L., Bariah, L., Centeno, A., Zoha, A., Arshad, K., Assaleh, K., Muhaidat, S., Debbah, M., and Imran, M. A.
• College/School: College of Science and Engineering > School of Engineering; College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity; College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
• Journal Name: IEEE Transactions on Emerging Topics in Computational Intelligence
• Publisher: IEEE
• ISSN: 2471-285X
• ISSN (Online): 2471-285X
• Published Online: 24 March 2023
• Copyright Holders: Copyright © 2023 IEEE
• First Published: First published in IEEE Transactions on Emerging Topics in Computational Intelligence 7(3): 957-979
• Publisher Policy: Reproduced in accordance with the publisher copyright policy