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Poster
Practical and Private (Deep) Learning Without Sampling or Shuffling
Peter Kairouz · Brendan McMahan · Shuang Song · Om Dipakbhai Thakkar · Abhradeep Guha Thakurta · Zheng Xu

Thu Jul 22 09:00 PM -- 11:00 PM (PDT) @
in Poster Session 6 »

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.

Keywords: Privacy, Anonymity, and Security · Social Aspects of Machine Learning

Author Information

Peter Kairouz (Google)
Brendan McMahan (Google)
Shuang Song (Google)
Om Dipakbhai Thakkar (Google)
Abhradeep Guha Thakurta (Google)
Zheng Xu (Google Research)

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