Privacy in AI remains a topic that draws attention from researchers and the
general public in recent years. As one way to implement privacy-preserving AI,
differentially private learning is a framework that enables AI models to use
differential privacy (DP). To achieve DP in the learning process, existing
algorithms typically limit the magnitude of gradients with a constant clipping,
which requires carefully tuned due to its significant impact on model
performance. As a solution to this issue, latest works NSGD and Auto-S
innovatively propose to use normalization instead of clipping to avoid
hyperparameter tuning. However, normalization-based approaches like NSGD and
Auto-S rely on a monotonic weight function, which imposes excessive weight on
small gradient samples and introduces extra deviation to the update. In this
paper, we propose a Differentially Private Per-Sample Adaptive Clipping
(DP-PSAC) algorithm based on a non-monotonic adaptive weight function, which
guarantees privacy without the typical hyperparameter tuning process of using a
constant clipping while significantly reducing the deviation between the update
and true batch-averaged gradient. We provide a rigorous theoretical convergence
analysis and show that with convergence rate at the same order, the proposed
algorithm achieves a lower non-vanishing bound, which is maintained over
training iterations, compared with NSGD/Auto-S. In addition, through extensive
experimental evaluation, we show that DP-PSAC outperforms or matches the
state-of-the-art methods on multiple main-stream vision and language tasks.

本文提出了一种基于自适应非单调权重函数的差分隐私逐样本自适应剪裁算法（DP-PSAC），通过一个严谨的理论收敛分析和若干个主流视觉和语言任务的实验验证，我们发现 DP-PSAC 能够同时保证差分隐私和显著降低更新值和真正批量平均梯度之间的偏差，其算法效果优于同领域的相关工作 NSGD/Auto-S。