Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Practical and Private (Deep) Learning without Sampling or Shuffling   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.
  The code is available at
https://github.com/google-research/federated/tree/master/dp_ftrl and
https://github.com/google-research/DP-FTRL .

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The categories of this paper are: cond-mat
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The categories of this paper are: comp-gas
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The categories of this paper are: mtrl-th
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The categories of this paper are: funct-an
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The categories of this paper are: chem-ph
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The categories of this paper are: adap-org
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The categories of this paper are: eess
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The categories of this paper are: hep-lat
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The categories of this paper are: astro-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: ao-sci
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The categories of this paper are: physics
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The categories of this paper are: econ
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The categories of this paper are: gr-qc
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The categories of this paper are: q-fin
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The categories of this paper are: stat