  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 cloudcentric
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-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.
