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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