DP-BandMF offers a powerful approach to differentially private machine learning, balancing privacy amplification with noise correlation for optimal noise reduction. However, its scalability has been limited to settings where the number of training iterations is less than $10^4$. In this work, we present techniques that significantly extend DP-BandMF's reach, enabling use in settings with and over $10^6$ training iterations. Our enhanced implementation, coupled with extensive experiments, provides clear guidelines on selecting the optimal number of bands. These insights offer practitioners a deeper understanding of DP-BandMF's performance and how to maximize its utility for privacy-preserving machine learning.

翻译：DP-BandMF为差分隐私机器学习提供了一种强大方法，通过平衡隐私放大与噪声相关性以实现最优噪声抑制。然而，其可扩展性此前受限于训练迭代次数小于$10^4$的场景。本研究提出一系列技术，显著拓展了DP-BandMF的适用范围，使其能够支持$10^6$及以上量级的训练迭代。我们通过增强型实现方案与大量实验，为带状数量的最优选择提供了明确指导。这些发现使实践者能更深入理解DP-BandMF的性能特征，并掌握在隐私保护机器学习中最大化其效用的方法。