Rethinking Clustered Federated Learning in NOMA Enhanced Wireless Networks

arXiv:2403.03157v1 Announce Type: cross
Abstract: This study explores the benefits of integrating the novel clustered federated learning (CFL) approach with non-orthogonal multiple access (NOMA) under non-independent and identically distributed (non-IID) datasets, where multiple devices participate in the aggregation with time limitations and a finite number of sub-channels. A detailed theoretical analysis of the generalization gap that measures the degree of non-IID in the data distribution is presented. Following that, solutions to address the challenges posed by non-IID conditions are proposed …

abstract aggregation analysis arxiv benefits channels cs.it cs.lg cs.ni datasets devices distributed federated learning independent limitations math.it multiple networks novel study type wireless