Federated Learning with Randomized Douglas-Rachford Splitting Methods

• URL: http://arxiv.org/abs/2103.03452v1
• Date: Fri, 5 Mar 2021 03:24:04 GMT
• Title: Federated Learning with Randomized Douglas-Rachford Splitting Methods
• Authors: Nhan H. Pham, Lam M. Nguyen, Dzung T. Phan, Quoc Tran-Dinh
• Abstract summary: We develop two new algorithms called, textbfFedDR and textbfDRaa. Our analysis shows that the new algorithms match the communication efficiency up to lower under lower assumptions.
• Score: 30.998685972581754
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we develop two new algorithms, called, \textbf{FedDR} and \textbf{asyncFedDR}, for solving a fundamental nonconvex optimization problem in federated learning. Our algorithms rely on a novel combination between a nonconvex Douglas-Rachford splitting method, randomized block-coordinate strategies, and asynchronous implementation. Unlike recent methods in the literature, e.g., FedSplit and FedPD, our algorithms update only a subset of users at each communication round, and possibly in an asynchronous mode, making them more practical. These new algorithms also achieve communication efficiency and more importantly can handle statistical and system heterogeneity, which are the two main challenges in federated learning. Our convergence analysis shows that the new algorithms match the communication complexity lower bound up to a constant factor under standard assumptions. Our numerical experiments illustrate the advantages of the proposed methods compared to existing ones using both synthetic and real datasets.

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