English(EN) Revisiting Distributed Sign-Based Variance Reduction

新方法提高分布式机器学习收敛速率

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-17 04:00

研究人员开发了一种新方法，以提高分布式机器学习环境中基于符号的方差缩减技术的收敛速率。这些方法对于降低通信成本至关重要，但在数据异构时可能存在偏差。所提出的解决方案通过对递归梯度增量进行无偏压缩，在服务器上跟踪全局梯度，从而实现了非凸随机和有限和优化问题的最优收敛速率。 AI

影响这项研究为分布式优化提供了理论上的进步，有望实现更大规模机器学习模型更高效的训练。

排序理由该条目是一篇学术论文，详细介绍了一种新的分布式机器学习方法。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv stat.ML TIER_1 English(EN) · Wei Jiang, Zechao Li, Lijun Zhang · 2026-09-17 04:00

重新审视基于符号的分布式方差缩减

arXiv:2609.18656v1 Announce Type: cross Abstract: Sign-based methods reduce communication costs in distributed environments, but aggregating local signs can introduce bias when data are heterogeneous. As a result, existing sign-based variance reduction methods fail to obtain the …