Per-example gradient clipping is a key algorithmic step that enables
practical differential private (DP) training for deep learning models. The
choice of clipping norm $R$, however, is shown to be vital for achieving high
accuracy under DP. We propose an easy-to-use replacement, called AutoClipping,
that eliminates the need to tune $R$ for any DP optimizers, including DP-SGD,
DP-Adam, DP-LAMB and many others. The automatic variants are as private and
computationally efficient as existing DP optimizers, but require no DP-specific
hyperparameters and thus make DP training as amenable as the standard
non-private training. We give a rigorous convergence analysis of automatic
DP-SGD in the non-convex setting, which shows that it enjoys an asymptotic
convergence rate that matches the standard SGD. We also demonstrate on various
language and vision tasks that automatic clipping outperforms or matches the
state-of-the-art, and can be easily employed with minimal changes to existing
codebases.

本论文提出了一种易于使用的替代方案 AutoClipping，它消除了任何 DP 优化器（包括 DP-SGD，DP-Adam，DP-LAMB 等）调整剪辑参数 R 的需要，提供了和现有 DP 优化器一样私密和计算有效的自动变体，但不需要 DP 特定的超参数，因此使 DP 训练像标准的非私密训练一样易于处理，在非凸环境中采用自动 DP-SGD 的严格收敛分析表明其具有与标准 SGD 相匹配的渐近收敛速度，并证明该算法在各种语言和视觉任务上优于或与最先进的方法相匹配，可以很容易地用最少的更改应用于现有的代码库。