Xincheng Xu

Enhancing DPSGD via Per-Sample Momentum and Low-Pass Filtering

Differentially Private Stochastic Gradient Descent (DPSGD) is widely used to train deep neural networks with formal privacy guarantees. However, the addition of differential privacy (DP) often degrades model accuracy by introducing both noise and bias. Existing techniques typically address only one