Distributed Stochastic Zeroth-Order Optimization with Compressed Communication
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909793849966592 |
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| author | Hua, Youqing Liu, Shuai Hong, Yiguang Ren, Wei |
| author_facet | Hua, Youqing Liu, Shuai Hong, Yiguang Ren, Wei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_17429 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Distributed Stochastic Zeroth-Order Optimization with Compressed Communication Hua, Youqing Liu, Shuai Hong, Yiguang Ren, Wei Optimization and Control Multiagent Systems 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. |
| title | Distributed Stochastic Zeroth-Order Optimization with Compressed Communication |
| topic | Optimization and Control Multiagent Systems |
| url | https://arxiv.org/abs/2503.17429 |