Improved Convergence Rates for Non-Convex Federated Learning with Compression

Rudrajit Das
Rudrajit Das
Abolfazl Hashemi
Abolfazl Hashemi
Cited by: 0|Bibtex|Views4
Other Links: arxiv.org

Abstract:

Federated learning is a new distributed learning paradigm that enables efficient training of emerging large-scale machine learning models. In this paper, we consider federated learning on non-convex objectives with compressed communication from the clients to the central server. We propose a novel first-order algorithm (\texttt{FedSTEPH...More

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