Scaling up Differentially Private Deep Learning with Fast Per-Example Gradient Clipping

TL;DR

This paper introduces fast, GPU-compatible methods for per-example gradient clipping in differentially private deep learning, significantly reducing training time and enabling more practical privacy-preserving models.

Contribution

It proposes new techniques for per-example gradient clipping that improve GPU efficiency and are compatible with auto-differentiation frameworks, accelerating private deep learning training.

Findings

Achieved 54x-94x speed-ups in training times.

Compatible with various neural network architectures.

Effective in reducing privacy-preserving training overhead.

Abstract

Recent work on Renyi Differential Privacy has shown the feasibility of applying differential privacy to deep learning tasks. Despite their promise, however, differentially private deep networks often lag far behind their non-private counterparts in accuracy, showing the need for more research in model architectures, optimizers, etc. One of the barriers to this expanded research is the training time -- often orders of magnitude larger than training non-private networks. The reason for this slowdown is a crucial privacy-related step called "per-example gradient clipping" whose naive implementation undoes the benefits of batch training with GPUs. By analyzing the back-propagation equations we derive new methods for per-example gradient clipping that are compatible with auto-differentiation (e.g., in PyTorch and TensorFlow) and provide better GPU utilization. Our implementation in PyTorch showed significant training speed-ups (by factors of 54x - 94x for training various models with batch sizes of 128). These techniques work for a variety of architectural choices including convolutional layers, recurrent networks, attention, residual blocks, etc.