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- W4310982091 abstract "Federated learning, as a branch of distributed learning, suffers from the high cost and concurrency of communication. The situation gets even worse in training deep models on large-scale mobile devices. Hence, communication efficiency is an avoidable topic in multi-party federated learning systems. Focusing on this issue, this chapter first discusses the background, the main methodologies and potential directions for communication efficient federated learning. Then, we introduce two methods for reducing the communication cost of federated learning, which are layer-wise asynchronous update and model quantization. Extensive experiments are performed to show the efficiency of these two approaches." @default.
- W4310982091 created "2022-12-22" @default.
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- W4310982091 date "2022-11-30" @default.
- W4310982091 modified "2023-10-16" @default.
- W4310982091 title "Communication Efficient Federated Learning" @default.
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- W4310982091 doi "https://doi.org/10.1007/978-981-19-7083-2_2" @default.
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