Work

Distributed Stochastic Zeroth-Order Optimization With Compressed Communication

Year: 2025

Type: article

Abstract: The dual challenges of high communication costs and gradient inaccessibility–common in privacy-sensitive systems or black-box environments–drive our work on communication-constrained, gradient-free distributed optimization. We propose a compressed distributed stochastic zeroth-order algorithm (Com-DSZO), which requires only two function evaluations per iteration and incorporates general compression operators. Rigorous analysis establishes a sublinear convergence rate for both smooth and nonsmooth objectives, explicitly characterizing the trade-off between compression and convergence. Furthermore, we develop a variance-reduced variant (VR-Com-DSZO) under stochastic mini-batch feedback. The effectiveness of the proposed algorithms is demonstrated through numerical experiments.

Source: IEEE Transactions on Automatic Control

DOI: 10.1109/tac.2025.3610109

Authors: Youqing Hua, Shuai Liu, Yiguang Hong, Wei Ren

Institutions: Shandong University, Tongji University, University of California, Riverside

Language: English

Topic: Stochastic Gradient Optimization Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: National Natural Science Foundation of China

Awards: G4229668996, G5642872638