Practical and Private (Deep) Learning Without Sampling or Shuffling

Peter Kairouz, Brendan Mcmahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, Zheng Xu
Proceedings of the 38th International Conference on Machine Learning, PMLR 139:5213-5225, 2021.

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 \emph{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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v139-kairouz21b, title = {Practical and Private (Deep) Learning Without Sampling or Shuffling}, author = {Kairouz, Peter and Mcmahan, Brendan and Song, Shuang and Thakkar, Om and Thakurta, Abhradeep and Xu, Zheng}, booktitle = {Proceedings of the 38th International Conference on Machine Learning}, pages = {5213--5225}, year = {2021}, editor = {Meila, Marina and Zhang, Tong}, volume = {139}, series = {Proceedings of Machine Learning Research}, month = {18--24 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v139/kairouz21b/kairouz21b.pdf}, url = {https://proceedings.mlr.press/v139/kairouz21b.html}, 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 \emph{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.} }
Endnote
%0 Conference Paper %T Practical and Private (Deep) Learning Without Sampling or Shuffling %A Peter Kairouz %A Brendan Mcmahan %A Shuang Song %A Om Thakkar %A Abhradeep Thakurta %A Zheng Xu %B Proceedings of the 38th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2021 %E Marina Meila %E Tong Zhang %F pmlr-v139-kairouz21b %I PMLR %P 5213--5225 %U https://proceedings.mlr.press/v139/kairouz21b.html %V 139 %X 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 \emph{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.
APA
Kairouz, P., Mcmahan, B., Song, S., Thakkar, O., Thakurta, A. & Xu, Z.. (2021). Practical and Private (Deep) Learning Without Sampling or Shuffling. Proceedings of the 38th International Conference on Machine Learning, in Proceedings of Machine Learning Research 139:5213-5225 Available from https://proceedings.mlr.press/v139/kairouz21b.html.

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