35 citations · 40 across the 5 of their papers we have counts for
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cs.LG2023★ 35 cited
Personalized Federated Learning with Feature Alignment and Classifier Collaboration
Jian Xu, Xinyi Tong, Shao-Lun Huang
Data heterogeneity is one of the most challenging issues in federated learning, which motivates a variety of approaches to learn personalized models for participating clients. One…
cs.LG2023★ 2 cited
Stabilizing and Improving Federated Learning with Non-IID Data and Client Dropout
Jian Xu, Meiling Yang, Wenbo Ding +1
The label distribution skew induced data heterogeniety has been shown to be a significant obstacle that limits the model performance in federated learning, which is particularly de…