A Lightweight Method for Tackling Unknown Participation Statistics in Federated Averaging

arXiv:2306.03401v3 Announce Type: replace
Abstract: In federated learning (FL), clients usually have diverse participation statistics that are unknown a priori, which can significantly harm the performance of FL if not handled properly. Existing works aiming at addressing this problem are usually based on global variance reduction, which requires a substantial amount of additional memory in a multiplicative factor equal to the total number of clients. An important open problem is to find a lightweight method for FL in the presence …

abstract arxiv cs.dc cs.it cs.lg diverse federated learning global harm math.it math.oc performance statistics stat.ml type variance