Towards Efficient and Scalable Implementation of Differentially Private Deep Learning

Sebastian Rodriguez Beltran, Marlon Tobaben, Joonas Jälkö, Niki Andreas Loppi, Antti Honkela

Keywords: differential privacy, gradient based optimization, computational efficiency, distributed computing

TL;DR: We study the computational efficiency of different implementations of differentially private stochastic gradient descent algorithm when implemented using proper Poisson subsampling.

Abstract: Differentially private stochastic gradient descent (DP-SGD) is the standard algorithm for training machine learning models under differential privacy (DP). The most common DP-SGD privacy accountants rely on Poisson subsampling for ensuring the theoretical DP guarantees. Implementing computationally efficient DP-SGD with Poisson subsampling is not trivial, which leads to many implementations ignoring this requirement. We conduct a comprehensive empirical study to quantify the computational cost of training deep learning models under DP given the requirement of Poisson subsampling, by re-implementing efficient methods using Poisson subsampling and benchmarking them. We find that using the naive implementation DP-SGD with Opacus in PyTorch has between 2.6 and 8 times lower throughput of processed training examples per second than SGD. However, efficient gradient clipping implementations with e.g. Ghost Clipping can roughly halve this cost. We propose alternative computationally efficient ways of implementing DP-SGD with JAX that are using Poisson subsampling and achieve only around 1.2 times lower throughput than SGD based on PyTorch. We highlight important implementation considerations with JAX. Finally, we study the scaling behaviour using up to 80 GPUs and find that DP-SGD scales better than SGD.

Primary Area: alignment, fairness, safety, privacy, and societal considerations

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Submission Number: 9746