AI RESEARCH
Achieving Linear Speedup for Composite Federated Learning
arXiv CS.LG
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ArXi:2602.03357v2 Announce Type: replace This paper proposes FedNMap, a normal map-based method for composite federated learning, where the objective consists of a smooth loss and a possibly nonsmooth regularizer. FedNMap leverages a normal map-based update scheme to handle the nonsmooth term and incorporates a local correction strategy to mitigate the impact of data heterogeneity across clients.