… and interference), limited wireless resources (eg, transmit power and radio spectrum), and
… study of how distributed learning can be efficiently and effectively deployed over wireless …

… distributed learning frameworks [23], [24], which have been extensively studied in both ML
and wireless communication … With wireless connectivity, distributed learning frameworks have …

… , wireless communication security and privacy issues have been ignored to some extent.
Since data security and privacy issues … an FL-based distributed learning architecture in 6G. In …

… due to its objective, which is parallelizing the gradient computation and aggregation across
multiple worker nodes, to distinguish this type of learning from the distributed learning that …

… This article has proposed a novel wireless CFL framework and introduced the challenges
and opportunities of using wireless communication techniques for optimizing CFL performance…

… with 6G application and the advantages of TL, this article aims to study two important
scientific problems. 1) Why does 6G wireless communications need TL? 2) How to use TL in 6G …

… distributed learning algorithms which enables devices to cooperatively build a unified learning
model … Therefore, it is hoped that this study on FL for wireless communications will provide …

… wireless communication in edge learning, collectively called learning-driven communication.
… His research interests include mobile edge computing, distributed learning, and 5G systems…

… problems of characterizing rate regions for communication networks supporting distributed
learning-and-computing tasks… EL techniques and wireless communication resource allocation…

… to train a learning model locally. One of the most promising of … distributed learning frameworks
is federated learning (FL) developed in [5]. FL is a distributed machine learning method …