Work

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

Year: 2026

Type: preprint

Abstract: 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 direction at communication iterations while keeping local updates noise-free. We establish an aggregate variable for tracking, characterize the limiting consensus point in the presence of added random noise, and prove convergence in mean under certain parameter conditions. We also establish infinite-horizon pure differential privacy for the complete communication transcript under an affine objective adjacency relation and geometrically decaying Laplace perturbations. Simulation results illustrate the advantages of the proposed method in terms of the convergence and optimization accuracy given a privacy budget.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.04854

Authors: Maithripala, Mihitha, Qiu, Chenyang, Lin, Zongli

Institution: University of Virginia

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, Division of Civil, Mechanical and Manufacturing Innovation

Awards: Collaborative Research: An Integrated Approach to Modeling, Decision-Making and Control for Energy Efficient Manufacturing, G6481325871

OpenAlex ID: W7220722351

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