June 6, 2024, 4:42 a.m. | Christopher A. Choquette-Choo, Arun Ganesh, Abhradeep Thakurta

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.02716v1 Announce Type: new
Abstract: The most common algorithms for differentially private (DP) machine learning (ML) are all based on stochastic gradient descent, for example, DP-SGD. These algorithms achieve DP by treating each gradient as an independent private query. However, this independence can cause us to overpay in privacy loss because we don't analyze the entire gradient trajectory. In this work, we propose a new DP algorithm, which we call Accelerated-DP-SRGD (DP stochastic recursive gradient descent), that enables us to …

abstract algorithms arxiv cs.cr cs.lg example gradient however independent machine machine learning privacy query stochastic type

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