Efficient Private SCO for Heavy-Tailed Data via Averaged Clipping
This work addresses the problem of inefficient privacy-preserving optimization for large-scale heavy-tailed data, offering incremental improvements over prior methods.
The paper tackles differentially private stochastic convex optimization for heavy-tailed data by proposing a one-time clipping strategy, achieving improved convergence results and complexity bounds for constrained and unconstrained problems, with numerical experiments validating the theoretical gains.
We consider stochastic convex optimization for heavy-tailed data with the guarantee of being differentially private (DP). Most prior works on differentially private stochastic convex optimization for heavy-tailed data are either restricted to gradient descent (GD) or performed multi-times clipping on stochastic gradient descent (SGD), which is inefficient for large-scale problems. In this paper, we consider a one-time clipping strategy and provide principled analyses of its bias and private mean estimation. We establish new convergence results and improved complexity bounds for the proposed algorithm called AClipped-dpSGD for constrained and unconstrained convex problems. We also extend our convergent analysis to the strongly convex case and non-smooth case (which works for generalized smooth objectives with H$\ddot{\text{o}}$lder-continuous gradients). All the above results are guaranteed with a high probability for heavy-tailed data. Numerical experiments are conducted to justify the theoretical improvement.