Wenfeng Du

FedWMSAM: Fast and Flat Federated Learning via Weighted Momentum and Sharpness-Aware Minimization

In federated learning (FL), models must \emph{converge quickly} under tight communication budgets while \emph{generalizing} across non-IID client distributions. These twin requirements have naturally led to two widely used techniques: client/server \emph{momentum} to accelerate progress, and \emph{s…