… 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 …

… , 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 …

… 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…

… applications of ML in wireless communication. This paper comprehensively surveys the recent
advances of the applications of ML in wireless communication… in the application layer. The …

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

… Thereafter, in Section 7, we provide a brief discussion on the recent application of ML to IoT
beyond wireless communication. Finally, the conclusion of this paper is provided in Section 8…

… 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 …

… problems. In Section VI we review recent deep learning applications to mobile and wireless
… scenarios ranging from mobile traffic analytics to security, and emerging applications. We …