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cs.LG2025
Analysis of Asynchronous Federated Learning: Unraveling the Interactions between Gradient Compression, Delay, and Data Heterogeneity
Diying Yang, Yingwei Hou, Weigang Wu
In practical federated learning (FL), the large communication overhead between clients and the server is often a significant bottleneck. Gradient compression methods can effectivel…
cs.LG2023
FedCME: Client Matching and Classifier Exchanging to Handle Data Heterogeneity in Federated Learning
Jun Nie, Danyang Xiao, Lei Yang +1
Data heterogeneity across clients is one of the key challenges in Federated Learning (FL), which may slow down the global model convergence and even weaken global model performance…
cs.LG2020★ 1 cited
PFL-MoE: Personalized Federated Learning Based on Mixture of Experts
Binbin Guo, Yuan Mei, Danyang Xiao +3
Federated learning (FL) is an emerging distributed machine learning paradigm that avoids data sharing among training nodes so as to protect data privacy. Under coordination of the…