Communication-Efficient Differentially Private Gradient Tracking for Distributed Optimization via Local Updates

This paper studies privacy-preserving distributed optimization using gradient tracking with one communication-free local update between consecutive communication rounds. Since local computation alone does not protect gradient information, we perturb both the primal state and the gradient-tracking di