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English(EN) How Edge of Stability Hinders SCAFFOLD in Federated Optimization

联邦优化：边缘稳定性阻碍SCAFFOLD算法

作者 PulseAugur 编辑部 · [1 个来源] ·

一项新的研究论文探讨了为什么SCAFFOLD算法在联邦优化中尽管有强大的理论保证，但在实践中通常表现不如更简单的FedAvg方法。研究提出，“边缘稳定性”（EoS）动力学，由于数据异质性而加剧，显著降低了SCAFFOLD准确估计全局梯度的能力。观察到这种退化与学习率成反比，并受数据异质性的影响，这表明SCAFFOLD在深度学习应用中存在关键限制。 AI

影响 识别出联邦学习算法的一个关键限制，可能指导未来研究朝着更鲁棒的优化方法发展。

排序理由 在arXiv上发表的研究论文，详细介绍了关于联邦优化算法的一项新发现。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

联邦优化：边缘稳定性阻碍SCAFFOLD算法

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在arXiv上发表的研究论文，详细介绍了关于联邦优化算法的一项新发现。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Anant Khandelwal, Michael Crawshaw, Mingrui Liu ·

    稳定性边界如何阻碍联邦优化中的 SCAFFOLD

    arXiv:2608.25873v1 Announce Type: new Abstract: In federated learning, it is well known that heterogeneous data can (in theory) slow down optimization, and much effort has been directed at designing optimization algorithms that are unaffected by data heterogeneity, such as the SC…

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