Robust Distributed Accelerated Stochastic Gradient Methods for Multi-Agent Networks

19 Oct 2019Alireza FallahMert GurbuzbalabanAsuman OzdaglarUmut SimsekliLingjiong Zhu

We study distributed stochastic gradient (D-SG) method and its accelerated variant (D-ASG) for solving decentralized strongly convex stochastic optimization problems where the objective function is distributed over several computational units, lying on a fixed but arbitrary connected communication graph, subject to local communication constraints where noisy estimates of the gradients are available. We develop a framework which allows to choose the stepsize and the momentum parameters of these algorithms in a way to optimize performance by systematically trading off the bias, variance, robustness to gradient noise and dependence to network effects... (read more)

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