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Distributed Stochastic Consensus Optimization Using Communication-Censoring Strategy
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DOI：10.1109/TCNS.2023.3281561.png)
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Abstract
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In this article, a novel communication-efficient distributed stochastic algorithm (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, the CO-DSA achieves a linear convergence rate even in the presence of an event-triggered communication-censoring strategy. Moreover, by properly setting the threshold function of the event-triggered communication scheme, the CO-DSA maintains the same convergence rate as the algorithm without event-triggered communication. This means the CO-DSA theoretically yields communication efficiency for free. Numerical experiments verify the theoretical findings and also show the excellent communication saving effect of the CO-DSA in large distributed networks.
Keywords:
Communication-censoring strategy
large- scale distributed optimization
linear convergence
stochastic gradient
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