9 citations · 23 across the 15 of their papers we have counts for
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cs.LG2023★ 5 cited
PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning
Mingjia Shi, Yuhao Zhou, Kai Wang +4
Classical federated learning (FL) enables training machine learning models without sharing data for privacy preservation, but heterogeneous data characteristic degrades the perform…
cs.DC2023
Federated cINN Clustering for Accurate Clustered Federated Learning
Yuhao Zhou, Minjia Shi, Yuxin Tian +3
Federated Learning (FL) presents an innovative approach to privacy-preserving distributed machine learning and enables efficient crowd intelligence on a large scale. However, a sig…
cs.LG2023
Communication-efficient Federated Learning with Single-Step Synthetic Features Compressor for Faster Convergence
Yuhao Zhou, Mingjia Shi, Yuanxi Li +3
Reducing communication overhead in federated learning (FL) is challenging but crucial for large-scale distributed privacy-preserving machine learning. While methods utilizing spars…