Distributed Stochastic Consensus Optimization Using Communication-Censoring Strategy

Abstract

In this paper, a novel communication-efficient distributed stochastic algorithm (referred as CO-DSA) is proposed for solving large-scale consensus optimization problems. As compared to the existing relevant work where only a sublinear convergence rate is obtained for strongly convex and smooth objective functions, CO-DSA achieves a linear convergence rate even in the presence of an event-triggered based communication-censoring strategy. Moreover, by properly setting the threshold function of the event-triggered communication scheme, CO-DSA maintains the same convergence rate as the algorithm without event-triggered communication. This means CO-DSA theoretically yields communication efficiency for free. Numerical experiments verify the theoretical findings and also show the excellent communication saving effect of CO-DSA in large distributed networks.