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arXiv · 2503.17429

Distributed Stochastic Zeroth-Order Optimization with Compressed Communication

Youqing Hua·Shuai Liu·Yiguang Hong·Wei Ren
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Abstract

The dual challenges of prohibitive communication overhead and the impracticality of gradient computation due to data privacy or black-box constraints in distributed systems motivate this work on communication-constrained gradient-free optimization. We propose a stochastic distributed zeroth-order algorithm (Com-DSZO) requiring only two function evaluations per iteration, integrated with general compression operators. Rigorous analysis establishes its sublinear convergence rate for both smooth and nonsmooth objectives, while explicitly elucidating the compression-convergence trade-off. Furthermore, we develop a variance-reduced variant (VR-Com-DSZO) under stochastic mini-batch feedback. The empirical algorithm performance are illustrated with numerical examples.

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Youqing Hua, Shuai Liu, Yiguang Hong, Wei Ren. 2025-03-21. Distributed Stochastic Zeroth-Order Optimization with Compressed Communication. https://doi.org/10.1109/tac.2025.3610109

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