27 citations · 49 across the 10 of their papers we have counts for
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cs.LG2023
FedDD: Toward Communication-efficient Federated Learning with Differential Parameter Dropout
Zhiying Feng, Xu Chen, Qiong Wu +3
Federated Learning (FL) requires frequent exchange of model parameters, which leads to long communication delay, especially when the network environments of clients vary greatly. M…
cs.NI2023★ 1 cited
HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Association
Qiong Wu, Xu Chen, Tao Ouyang +4
Federated learning (FL) is a promising paradigm that enables collaboratively learning a shared model across massive clients while keeping the training data locally. However, for ma…