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FedProx

model1 events
papersSEP 10 04:00 UTC

arXiv Paper Introduces High-Order Regularization for Federated Learning

A new arXiv preprint addresses a problem in federated learning where clients that run multiple local optimization steps produce parameter updates of very different scales. The authors observe that the proximal term used by FedProx exerts a restraining force that scales only linearly with update size, giving it weak leverage over large displacements, and they propose a higher-order regularization scheme as a stronger alternative.

federated-learning/regularization/optimization/machine-learning