Decentralized Online Strongly Convex Optimization with General Compressors and Random Disturbances

Honglei Liu (), Deming Yuan () and Baoyong Zhang ()
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Honglei Liu: Nanjing University of Science and Technology
Deming Yuan: Nanjing University of Science and Technology
Baoyong Zhang: Nanjing University of Science and Technology

Journal of Optimization Theory and Applications, 2025, vol. 204, issue 1, No 6, 22 pages

Abstract: Abstract This paper considers the decentralized online strongly convex optimization over a multi-agent network, where the objective is to minimize a global loss function accumulated by the local loss functions of all agents. The Time-Varying Scaling Compression method is applied to deal with the communication bottleneck in the presence of disturbances. Then, by using the scaling compression, a decentralized online algorithm is proposed and the convergence results of the algorithm are analyzed. By choosing proper parameters, a sublinear regret can be obtained, which matches the same order as those of algorithms with no disturbances. Finally, numerical simulations are given to demonstrate the efficiency of the proposed algorithm.

Keywords: Decentralized online optimization; Strongly convex optimization; Disturbances; Scaling compression (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10957-024-02595-z

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