Towards efficient communications in federated learning: A contemporary survey

• Published in: Journal of the Franklin Institute Vol. 360; no. 12; pp. 8669 - 8703
• Main Authors: Zhao, Zihao, Mao, Yuzhu, Liu, Yang, Song, Linqi, Ouyang, Ye, Chen, Xinlei, Ding, Wenbo
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.08.2023
• ISSN: 0016-0032; 1879-2693
• DOI: 10.1016/j.jfranklin.2022.12.053

In the traditional distributed machine learning scenario, the user’s private data is transmitted between clients and a central server, which results in significant potential privacy risks. In order to balance the issues of data privacy and joint training of models, federated learning (FL) is proposed as a particular distributed machine learning procedure with privacy protection mechanisms, which can achieve multi-party collaborative computing without revealing the original data. However, in practice, FL faces a variety of challenging communication problems. This review seeks to elucidate the relationship between these communication issues by methodically assessing the development of FL communication research from three perspectives: communication efficiency, communication environment, and communication resource allocation. Firstly, we sort out the current challenges existing in the communications of FL. Second, we have collated FL communications-related papers and described the overall development trend of the field based on their logical relationship. Ultimately, we discuss the future directions of research for communications in FL.