PhD student, Hong Kong University of Science and Technology
1 paper at NeurIPS 2025
We propose FedWMSAM, a novel method that combines personalized momentum and dynamic SAM to achieve faster convergence and flatter minima in federated learning, effectively addressing client drift and data heterogeneity.
