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arXiv · 2504.21693

Distributed Online Randomized Gradient-Free optimization with Compressed Communication

Longkang Zhu·Xinli Shi·Xiangping Xu·Jinde Cao
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Abstract

This paper addresses two fundamental challenges in distributed online convex optimization: communication efficiency and optimization under limited feedback. We propose Online Compressed Gradient Tracking with one-point Bandit Feedback (OCGT-BF), a novel algorithm that harness data compression and gradient-free optimization techniques in distributed networks. Our algorithm incorporates a compression scheme with error compensation mechanisms to reduce communication overhead while maintaining convergence guarantees. Unlike traditional approaches that assume perfect communication and full gradient access, OCGT-BF operates effectively under practical constraints by combining gradient-like tracking with one-point feedback estimation. We provide theoretical analysis demonstrating the dynamic regret bounds under both bandit feedback and stochastic gradient scenarios. Finally, extensive experiments validate that OCGT-BF achieves low dynamic regret while significantly reducing communication requirements.

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Longkang Zhu, Xinli Shi, Xiangping Xu, Jinde Cao. 2025-04-30. Distributed Online Randomized Gradient-Free optimization with Compressed Communication. https://arxiv.org/abs/2504.21693

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