S-NEAR-DGD: A Flexible Distributed Stochastic Gradient Method for Inexact Communication
From MaRDI portal
Publication:6093049
DOI10.1109/TAC.2022.3151734arXiv2102.00121OpenAlexW3128503924MaRDI QIDQ6093049
Publication date: 6 September 2023
Published in: IEEE Transactions on Automatic Control (Search for Journal in Brave)
Abstract: We present and analyze a stochastic distributed method (S-NEAR-DGD) that can tolerate inexact computation and inaccurate information exchange to alleviate the problems of costly gradient evaluations and bandwidth-limited communication in large-scale systems. Our method is based on a class of flexible, distributed first order algorithms that allow for the trade-off of computation and communication to best accommodate the application setting. We assume that all the information exchange between nodes is subject to random distortion and that only stochastic approximations of the true gradients are available. Our theoretical results prove that the proposed algorithm converges linearly in expectation to a neighborhood of the optimal solution for strongly convex objective functions with Lipschitz gradients. We characterize the dependence of this neighborhood on algorithm and network parameters, the quality of the communication channel and the precision of the stochastic gradient approximations used. Finally, we provide numerical results to evaluate the empirical performance of our method.
Full work available at URL: https://arxiv.org/abs/2102.00121
This page was built for publication: S-NEAR-DGD: A Flexible Distributed Stochastic Gradient Method for Inexact Communication
