June 11, 2024, 4:45 a.m. | Milad Sefidgaran, Romain Chor, Abdellatif Zaidi, Yijun Wan

stat.ML updates on arXiv.org arxiv.org

arXiv:2306.05862v2 Announce Type: replace
Abstract: We investigate the generalization error of statistical learning models in a Federated Learning (FL) setting. Specifically, we study the evolution of the generalization error with the number of communication rounds $R$ between $K$ clients and a parameter server (PS), i.e., the effect on the generalization error of how often the clients' local models are aggregated at PS. In our setup, the more the clients communicate with PS the less data they use for local training …

abstract analysis arxiv communication cs.it cs.lg error evolution federated learning math.it replace rounds server statistical stat.ml study type you

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