Scaling Private Deep Learning with Low-Rank and Sparse Gradients
This addresses the problem of training private deep learning models efficiently for applications requiring data privacy, representing an incremental improvement over existing methods.
The paper tackles the challenge of applying Differentially Private Stochastic Gradient Descent (DPSGD) to large-scale neural networks by proposing a framework that exploits low-rank and sparse gradient structures to reduce noise and improve performance, showing empirical gains on NLP and vision tasks.
Applying Differentially Private Stochastic Gradient Descent (DPSGD) to training modern, large-scale neural networks such as transformer-based models is a challenging task, as the magnitude of noise added to the gradients at each iteration scales with model dimension, hindering the learning capability significantly. We propose a unified framework, $\textsf{LSG}$, that fully exploits the low-rank and sparse structure of neural networks to reduce the dimension of gradient updates, and hence alleviate the negative impacts of DPSGD. The gradient updates are first approximated with a pair of low-rank matrices. Then, a novel strategy is utilized to sparsify the gradients, resulting in low-dimensional, less noisy updates that are yet capable of retaining the performance of neural networks. Empirical evaluation on natural language processing and computer vision tasks shows that our method outperforms other state-of-the-art baselines.