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arXiv · 2601.22334

DP-{\lambda}CGD: Efficient Noise Correlation for Differentially Private Model Training

Nikita P. Kalinin·Ryan McKenna·Rasmus Pagh·Christoph H. Lampert
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Abstract

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.

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Nikita P. Kalinin, Ryan McKenna, Rasmus Pagh, Christoph H. Lampert. 2026-01-29. DP-{\lambda}CGD: Efficient Noise Correlation for Differentially Private Model Training. https://arxiv.org/abs/2601.22334

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