Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscape

arXiv:2305.11584v2 Announce Type: replace
Abstract: In federated learning (FL), a cluster of local clients are chaired under the coordination of the global server and cooperatively train one model with privacy protection. Due to the multiple local updates and the isolated non-iid dataset, clients are prone to overfit into their own optima, which extremely deviates from the global objective and significantly undermines the performance. Most previous works only focus on enhancing the consistency between the local and global objectives to alleviate …

abstract arxiv cluster cs.dc cs.lg dataset dynamic federated learning global landscape math.oc multiple one model privacy protection server train type updates