Special Topic on Federated Learning over Wireless Networks

Fepderated learning has revolutionized the way we ap-roach machine learning by enabling multiple edge de-vices to collaboratively learn a shared machine learn-ing model without the need for centralized data collec-tion. Such a new machine learning paradigm has gained sig-nificant attention in recent years due to its ability to address privacy and security concerns associated with centralized learning, as well as its potential to reduce communication overhead and improve scalability. Deploying cross-device fed-erated learning at the network edge over wireless networks has further extended its potential due to the close proximity to the gigantic number of mobile data and computing power provided by the surging number of Internet of Things (IoT) devices, and is expected to breed new intelligent applications that demand delay-sensitive and mission-critical services, such as smart in-dustry, auto-driving, and metaverse. Despite its great promise, the successful deployment of federated learning over wireless networks has also presented its own unique set of challenges, including network heterogeneity, communication delays, and unreliable connections.