Federated learning becomes increasingly attractive in the areas of wireless
communications and machine learning due to its powerful functions and potential
applications. In contrast to other machine learning tools that require no
communication resources, federated learning exploits communications between the
central server and the distributed local clients to train and optimize a
machine learning model. Therefore, how to efficiently assign limited
communication resources to train a federated learning model becomes critical to
performance optimization. On the other hand, federated learning, as a brand new
tool, can potentially enhance the intelligence of wireless networks. In this
article, we provide a comprehensive overview on the relationship between
federated learning and wireless communications, including basic principle of
federated learning, efficient communications for training a federated learning
model, and federated learning for intelligent wireless applications. We also
identify some future research challenges and directions at the end of this
article.

本篇文章探讨了联邦学习与无线通信之间的关系，包括联邦学习的基本原理、训练模型的有效通信、联邦学习对智能无线应用的潜在贡献以及未来研究挑战和方向。