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- W4385377094 abstract "Federated learning is a distributed machine learning approach that keeps data locally while achieving the utilization of fragmented data and protecting client privacy to a certain extent. However, the existence of data heterogeneity may cause instability and low efficiency during federated learning training, while system heterogeneity may lead to resource waste and low efficiency. Meanwhile, due to limited communication bandwidth and other resources, selecting the participating client in the training process becomes significant. Research has shown that a reasonable client selection mechanism can improve training efficiency and model accuracy. This article discusses the heterogeneity faced by federated learning in heterogeneous environments and the highly dynamic challenges it will face in the future. It reviews the latest client selection mechanisms from the perspective of client reputation, time threshold, and other factors. Finally, it provides some research directions for federated learning client selection." @default.
- W4385377094 created "2023-07-30" @default.
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- W4385377094 date "2023-01-01" @default.
- W4385377094 modified "2023-09-27" @default.
- W4385377094 title "A Review of Client Selection Mechanisms in Heterogeneous Federated Learning" @default.
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- W4385377094 doi "https://doi.org/10.1007/978-981-99-4742-3_63" @default.
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