most citedUnderstanding Generalization of Federated Learning: the Trade-off between Model Stability and Optimization

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cs.LG2024★ 2 cited

Understanding Generalization of Federated Learning: the Trade-off between Model Stability and Optimization

Dun Zeng, Zheshun Wu, Shiyu Liu +3

Federated Learning (FL) is a distributed learning approach that trains machine learning models across multiple devices while keeping their local data private. However, FL often fac…

cs.LG2023★ 1 cited

On the Power of Adaptive Weighted Aggregation in Heterogeneous Federated Learning and Beyond

Dun Zeng, Zenglin Xu, Shiyu Liu +3

Federated averaging (FedAvg) is the most fundamental algorithm in Federated learning (FL). Previous theoretical results assert that FedAvg convergence and generalization degenerate…

cs.LG2023

Enhanced Federated Optimization: Adaptive Unbiased Client Sampling with Reduced Variance

Dun Zeng, Zenglin Xu, Yu Pan +3

Federated Learning (FL) is a distributed learning paradigm to train a global model across multiple devices without collecting local data. In FL, a server typically selects a subset…