1. Towards efficient communications in federated learning: A contemporary survey.
- Author
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Zhao, Zihao, Mao, Yuzhu, Liu, Yang, Song, Linqi, Ouyang, Ye, Chen, Xinlei, and Ding, Wenbo
- Subjects
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DATA privacy , *MACHINE learning , *COMMUNICATION barriers , *RESOURCE allocation , *COMMUNICATIONS research - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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