Advocating for the Silent: Enhancing Federated Generalization for Non-Participating Clients

arXiv:2310.07171v4 Announce Type: replace
Abstract: Federated Learning (FL) has surged in prominence due to its capability of collaborative model training without direct data sharing. However, the vast disparity in local data distributions among clients, often termed the Non-Independent Identically Distributed (Non-IID) challenge, poses a significant hurdle to FL's generalization efficacy. The scenario becomes even more complex when not all clients participate in the training process, a common occurrence due to unstable network connections or limited computational capacities. This can greatly …

abstract arxiv capability challenge collaborative cs.it cs.lg data data sharing distributed federated learning independent math.it training type vast