Steps Adaptive Decay DPSGD: Enhancing Performance on Imbalanced Datasets with Differential Privacy with HAM10000
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908441717506048 |
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| author | Huang, Xiaobo Xie, Fang |
| author_facet | Huang, Xiaobo Xie, Fang |
| contents | When applying machine learning to medical image classification, data leakage is a critical issue. Previous methods, such as adding noise to gradients for differential privacy, work well on large datasets like MNIST and CIFAR-100, but fail on small, imbalanced medical datasets like HAM10000. This is because the imbalanced distribution causes gradients from minority classes to be clipped and lose crucial information, while majority classes dominate. This leads the model to fall into suboptimal solutions early. To address this, we propose SAD-DPSGD, which uses a linear decaying mechanism for noise and clipping thresholds. By allocating more privacy budget and using higher clipping thresholds in the initial training phases, the model avoids suboptimal solutions and enhances performance. Experiments show that SAD-DPSGD outperforms Auto-DPSGD on HAM10000, improving accuracy by 2.15% under $ε= 3.0$ , $δ= 10^{-3}$. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_06619 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Steps Adaptive Decay DPSGD: Enhancing Performance on Imbalanced Datasets with Differential Privacy with HAM10000 Huang, Xiaobo Xie, Fang Machine Learning When applying machine learning to medical image classification, data leakage is a critical issue. Previous methods, such as adding noise to gradients for differential privacy, work well on large datasets like MNIST and CIFAR-100, but fail on small, imbalanced medical datasets like HAM10000. This is because the imbalanced distribution causes gradients from minority classes to be clipped and lose crucial information, while majority classes dominate. This leads the model to fall into suboptimal solutions early. To address this, we propose SAD-DPSGD, which uses a linear decaying mechanism for noise and clipping thresholds. By allocating more privacy budget and using higher clipping thresholds in the initial training phases, the model avoids suboptimal solutions and enhances performance. Experiments show that SAD-DPSGD outperforms Auto-DPSGD on HAM10000, improving accuracy by 2.15% under $ε= 3.0$ , $δ= 10^{-3}$. |
| title | Steps Adaptive Decay DPSGD: Enhancing Performance on Imbalanced Datasets with Differential Privacy with HAM10000 |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2507.06619 |