2 papers
cs.LG2025
A Fast and Flat Federated Learning Method via Weighted Momentum and Sharpness-Aware Minimization
Tianle Li, Yongzhi Huang, Linshan Jiang +5
In federated learning (FL), models must \emph{converge quickly} under tight communication budgets while \emph{generalizing} across non-IID client distributions. These twin requirem…
cs.LG2025
FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios
Tianle Li, Yongzhi Huang, Linshan Jiang +5
Federated Learning (FL) enables decentralized model training while preserving data privacy. Despite its benefits, FL faces challenges with non-identically distributed (non-IID) dat…