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Distributed Stochastic Consensus Optimization Using Communication-Censoring Strategy
  • ranran li ,
  • Weicheng Xu ,
  • Fan Yu
ranran li
Northeastern University

Corresponding Author:[email protected]

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Weicheng Xu
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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 eventtriggered 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.
2023Published in IEEE Transactions on Control of Network Systems on pages 1-12. 10.1109/TCNS.2023.3281561