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News/Mitigating Disparate Impact of Differentially Private Learning through Bounded Adaptive Clipping
arxiv
PublishedJune 12, 2026 at 4:00 AM
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Mitigating Disparate Impact of Differentially Private Learning through Bounded Adaptive Clipping

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arXiv:2506.01396v2 Announce Type: replace Abstract: Differential privacy (DP) has become an essential framework for privacy-preserving machine learning. Existing DP learning methods, however, often have disparate impacts on model predictions, e.g., for minority groups. Gradient clipping, which is of

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