DP-λCGD: Efficient Noise Correlation for Differentially Private Model Training
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866910209636564992 |
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| author | Kalinin, Nikita P. McKenna, Ryan Pagh, Rasmus Lampert, Christoph H. |
| author_facet | Kalinin, Nikita P. McKenna, Ryan Pagh, Rasmus Lampert, Christoph H. |
| contents | Differentially private stochastic gradient descent (DP-SGD) is the gold standard for training machine learning models with formal differential privacy guarantees. Several recent extensions improve its accuracy by introducing correlated noise across training iterations. Matrix factorization mechanisms are a prominent example, but they correlate noise across many iterations and require storing previously added noise vectors, leading to substantial memory overhead in some settings. In this work, we propose a new noise correlation strategy that correlates noise only with the immediately preceding iteration and cancels a controlled portion of it. Our method relies on noise regeneration using a pseudorandom noise generator, eliminating the need to store past noise. As a result, it requires no additional memory beyond standard DP-SGD. We show that the computational overhead is minimal and empirically demonstrate improved accuracy over DP-SGD. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_22334 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | DP-λCGD: Efficient Noise Correlation for Differentially Private Model Training Kalinin, Nikita P. McKenna, Ryan Pagh, Rasmus Lampert, Christoph H. Machine Learning Differentially private stochastic gradient descent (DP-SGD) is the gold standard for training machine learning models with formal differential privacy guarantees. Several recent extensions improve its accuracy by introducing correlated noise across training iterations. Matrix factorization mechanisms are a prominent example, but they correlate noise across many iterations and require storing previously added noise vectors, leading to substantial memory overhead in some settings. In this work, we propose a new noise correlation strategy that correlates noise only with the immediately preceding iteration and cancels a controlled portion of it. Our method relies on noise regeneration using a pseudorandom noise generator, eliminating the need to store past noise. As a result, it requires no additional memory beyond standard DP-SGD. We show that the computational overhead is minimal and empirically demonstrate improved accuracy over DP-SGD. |
| title | DP-λCGD: Efficient Noise Correlation for Differentially Private Model Training |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2601.22334 |