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- W4313627870 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." @default.
- W4313627870 created "2023-01-07" @default.
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- W4313627870 date "2023-08-01" @default.
- W4313627870 modified "2023-09-30" @default.
- W4313627870 title "Towards efficient communications in federated learning: A contemporary survey" @default.
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- W4313627870 doi "https://doi.org/10.1016/j.jfranklin.2022.12.053" @default.
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