Differentially Private Sharpness-Aware Training
Jinseong Park Hoki Kim Yujin Choi Jaewook Lee
Training deep learning models with differential privacy (DP) results in a
degradation of performance. The training dynamics of models with DP show a
significant difference from standard training, whereas understanding the
geometric properties of private learning remains largely unexplored. In this
paper, we investigate sharpness, a key factor in achieving better
generalization, in private learning. We show that flat minima can help reduce
the negative effects of per-example gradient clipping and the addition of
Gaussian noise. We then verify the effectiveness of Sharpness-Aware
Minimization (SAM) for seeking flat minima in private learning. However, we
also discover that SAM is detrimental to the privacy budget and computational
time due to its two-step optimization. Thus, we propose a new sharpness-aware
training method that mitigates the privacy-optimization trade-off. Our
experimental results demonstrate that the proposed method improves the
performance of deep learning models with DP from both scratch and fine-tuning.
Code is available at https://github.com/jinseongP/DPSAT.