AI RESEARCH
Unveiling High-Probability Generalization in Decentralized SGD
arXiv CS.LG
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ArXi:2605.10205v1 Announce Type: new Decentralized stochastic gradient descent (D-SGD) is an efficient method for large-scale distributed learning. Existing generalization studies mainly address expected results, achieving rates limited to $\mathcal{O}\left(\frac{1}{\delta \sqrt{mn}}\right)$, where $\delta$ is the confidence parameter, $m$ the number of workers, and $n$ the sample size. When $m=1$, D-SGD reduces to traditional SGD, whose optimal high-probability generalization bound is $\mathcal{O}\left(\frac{1}{\sqrt{n}}\log (1/\delta)\right.