Practical and Private (Deep) Learning without Sampling or Shuffling

• URL: http://arxiv.org/abs/2103.00039v1
• Date: Fri, 26 Feb 2021 20:16:26 GMT
• Title: Practical and Private (Deep) Learning without Sampling or Shuffling
• Authors: Peter Kairouz, Brendan McMahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, Zheng Xu
• Abstract summary: We consider training models with differential privacy using mini-batch gradients. We analyze a DP variant of Follow-The-Regularized-Leader (DP-FTRL) that compares favorably to amplified DP-SGD.
• Score: 24.29228578925639
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We consider training models with differential privacy (DP) using mini-batch gradients. The existing state-of-the-art, Differentially Private Stochastic Gradient Descent (DP-SGD), requires privacy amplification by sampling or shuffling to obtain the best privacy/accuracy/computation trade-offs. Unfortunately, the precise requirements on exact sampling and shuffling can be hard to obtain in important practical scenarios, particularly federated learning (FL). We design and analyze a DP variant of Follow-The-Regularized-Leader (DP-FTRL) that compares favorably (both theoretically and empirically) to amplified DP-SGD, while allowing for much more flexible data access patterns. DP-FTRL does not use any form of privacy amplification.

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