Practical and Private (Deep) Learning Without Sampling or Shuffling

We consider training models with differential privacy (DP) using mini-batch gradients. The existing state-ofthe-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. * Google. kairouz, mcmahan, shuangsong, omthkkr, athakurta, xuzheng@google.com 1 Gradient computed on a subset of the training examples, also called a mini-batch.