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Curvature-Aware Clipping: Mitigating Bias in DPSGD via Approximate Second-Order Information
Differentially Private Stochastic Gradient Descent (DPSGD) has become the de facto standard for training deep neural networks with rigorous privacy guarantees. However, the utility of DPSGD is constrained by gradient clipping bias and DP noise. While existing work has tried to address these issues,
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