On the Performance of Differentially Private Optimization with Heavy-Tail Class Imbalance
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908449223213056 |
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| author | Tang, Qiaoyue Zhiyanov, Alain Lécuyer, Mathias |
| author_facet | Tang, Qiaoyue Zhiyanov, Alain Lécuyer, Mathias |
| contents | In this work, we analyze the optimization behaviour of common private learning optimization algorithms under heavy-tail class imbalanced distribution. We show that, in a stylized model, optimizing with Gradient Descent with differential privacy (DP-GD) suffers when learning low-frequency classes, whereas optimization algorithms that estimate second-order information do not. In particular, DP-AdamBC that removes the DP bias from estimating loss curvature is a crucial component to avoid the ill-condition caused by heavy-tail class imbalance, and empirically fits the data better with $\approx8\%$ and $\approx5\%$ increase in training accuracy when learning the least frequent classes on both controlled experiments and real data respectively. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_10536 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | On the Performance of Differentially Private Optimization with Heavy-Tail Class Imbalance Tang, Qiaoyue Zhiyanov, Alain Lécuyer, Mathias Machine Learning In this work, we analyze the optimization behaviour of common private learning optimization algorithms under heavy-tail class imbalanced distribution. We show that, in a stylized model, optimizing with Gradient Descent with differential privacy (DP-GD) suffers when learning low-frequency classes, whereas optimization algorithms that estimate second-order information do not. In particular, DP-AdamBC that removes the DP bias from estimating loss curvature is a crucial component to avoid the ill-condition caused by heavy-tail class imbalance, and empirically fits the data better with $\approx8\%$ and $\approx5\%$ increase in training accuracy when learning the least frequent classes on both controlled experiments and real data respectively. |
| title | On the Performance of Differentially Private Optimization with Heavy-Tail Class Imbalance |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2507.10536 |