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arXiv · 2212.03080

Straggler-Resilient Differentially-Private Decentralized Learning

Yauhen Yakimenka·Chung-Wei Weng·Hsuan-Yin Lin·Eirik Rosnes·Jörg Kliewer
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Abstract

We consider the straggler problem in decentralized learning over a logical ring while preserving user data privacy. Especially, we extend the recently proposed framework of differential privacy (DP) amplification by decentralization by Cyffers and Bellet to include overall training latency--comprising both computation and communication latency. Analytical results on both the convergence speed and the DP level are derived for both a skipping scheme (which ignores the stragglers after a timeout) and a baseline scheme that waits for each node to finish before the training continues. A trade-off between overall training latency, accuracy, and privacy, parameterized by the timeout of the skipping scheme, is identified and empirically validated for logistic regression on a real-world dataset and for image classification using the MNIST and CIFAR-10 datasets.

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Yauhen Yakimenka, Chung-Wei Weng, Hsuan-Yin Lin, Eirik Rosnes, Jörg Kliewer. 2024-06-28. Straggler-Resilient Differentially-Private Decentralized Learning. https://arxiv.org/abs/2212.03080

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