Web: http://arxiv.org/abs/2204.13650

April 29, 2022, 1:20 a.m. | Soham De, Leonard Berrada, Jamie Hayes, Samuel L. Smith, Borja Balle

cs.CR updates on arXiv.org arxiv.org

Differential Privacy (DP) provides a formal privacy guarantee preventing
adversaries with access to a machine learning model from extracting information
about individual training points. Differentially Private Stochastic Gradient
Descent (DP-SGD), the most popular DP training method, realizes this protection
by injecting noise during training. However previous works have found that
DP-SGD often leads to a significant degradation in performance on standard
image classification benchmarks. Furthermore, some authors have postulated that
DP-SGD inherently performs poorly on large models, since the norm …

classification lg scale

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