Differentially private training of neural networks with Langevin dynamics forcalibrated predictive uncertainty

• URL: http://arxiv.org/abs/2107.04296v1
• Date: Fri, 9 Jul 2021 08:14:45 GMT
• Title: Differentially private training of neural networks with Langevin dynamics forcalibrated predictive uncertainty
• Authors: Moritz Knolle, Alexander Ziller, Dmitrii Usynin, Rickmer Braren, Marcus R. Makowski, Daniel Rueckert, Georgios Kaissis
• Abstract summary: We show that differentially private gradient descent (DP-SGD) can yield poorly calibrated, overconfident deep learning models. This represents a serious issue for safety-critical applications, e.g. in medical diagnosis.
• Score: 58.730520380312676
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We show that differentially private stochastic gradient descent (DP-SGD) can yield poorly calibrated, overconfident deep learning models. This represents a serious issue for safety-critical applications, e.g. in medical diagnosis. We highlight and exploit parallels between stochastic gradient Langevin dynamics, a scalable Bayesian inference technique for training deep neural networks, and DP-SGD, in order to train differentially private, Bayesian neural networks with minor adjustments to the original (DP-SGD) algorithm. Our approach provides considerably more reliable uncertainty estimates than DP-SGD, as demonstrated empirically by a reduction in expected calibration error (MNIST $\sim{5}$-fold, Pediatric Pneumonia Dataset $\sim{2}$-fold).

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