Understanding Server-Assisted Federated Learning in the Presence of Incomplete Client Participation

arXiv:2405.02745v1 Announce Type: new
Abstract: Existing works in federated learning (FL) often assume an ideal system with either full client or uniformly distributed client participation. However, in practice, it has been observed that some clients may never participate in FL training (aka incomplete client participation) due to a myriad of system heterogeneity factors. A popular approach to mitigate impacts of incomplete client participation is the server-assisted federated learning (SA-FL) framework, where the server is equipped with an auxiliary dataset. However, …

abstract arxiv client cs.dc cs.lg distributed federated learning however practice server training type understanding