Privacy protection and nonconvexity are two challenging problems in decentralized
optimization and learning involving sensitive data. Despite some recent advances …

With decentralized optimization having increased applications in various domains ranging
from machine learning, control, to robotics, its privacy is also receiving increased attention …

Privacy protection has become an increasingly pressing requirement in distributed
optimization. However, equipping distributed optimization with differential privacy, the state …

In this work, we study the decentralized empirical risk minimization problem under the
constraint of differential privacy (DP). Based on the algorithmic framework of dual averaging …

Privacy concerns with sensitive data are receiving increasing attention. In this paper, we
study local differential privacy (LDP) in interactive decentralized optimization. By …

While many solutions for privacy-preserving convex empirical risk minimization (ERM) have
been developed, privacy-preserving nonconvex ERM remains a challenge. We study …

This work studies the distributed empirical risk minimization (ERM) problem under
differential privacy (DP) constraint. Standard distributed algorithms achieve DP typically by …

By enabling multiple agents to cooperatively solve a global optimization problem in the
absence of a central coordinator, decentralized stochastic optimization is gaining increasing …

Decentralized stochastic optimization is the basic building block of modern collaborative
machine learning, distributed estimation and control, and large-scale sensing. Since …

We study gradient descent under linearly correlated noise. Our work is motivated by recent
practical methods for optimization with differential privacy (DP), such as DP-FTRL, which …