English(EN) Variance Reduction for Decentralized Stochastic Optimization with Multiple Local Updates

新的去中心化优化算法有望实现更快的收敛速度和更低的通信成本

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-02 05:00

开发了一种新的去中心化优化算法，该算法将方差缩减与多次局部更新相结合。该方法提供了独立于网络大小且与局部更新次数成反比的步长，从而可以获得更大的步长和更快的收敛速度。在强凸环境下，该算法在随机梯度下降的去中心化优化中，实现了首次已知的期望线性收敛保证，同时还将通信成本降低了一半。 AI

影响这项研究通过改进去中心化优化技术，有望实现更大规模人工智能模型的更高效训练。

排序理由该集群包含一篇详细介绍新算法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Jie Liu, Nicola Bastianello, Wenying Xu, Daniel W. C. Ho · 2026-11-02 05:00

Variance Reduction for Decentralized Stochastic Optimization with Multiple Local Updates

arXiv:2610.41927v1 Announce Type: cross Abstract: Multiple local updates are widely used in decentralized optimization to enhance communication efficiency, often in conjunction with stochastic gradients when exact gradient computation is costly. However, such algorithms generally…