Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges (2111.07392v2)

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 AI opens up new horizons for providing ubiquitous data-driven intelligent services. Traditional cloudcentric 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-ofthe-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.