Privacy-preserving Decentralized Optimization via Decomposition

TL;DR

This paper introduces a privacy-preserving decentralized optimization method that enables agents to collaboratively minimize a global function without revealing their private local objectives, using a novel proximal Jacobian ADMM approach.

Contribution

It proposes a new decentralized optimization algorithm based on function decomposition and proximal Jacobian ADMM to enhance privacy preservation.

Findings

Numerical simulations demonstrate the effectiveness of the proposed method.

The approach successfully prevents leakage of private local objective information.

The method maintains optimization accuracy comparable to non-privacy-preserving approaches.

Abstract

This paper considers the problem of privacy-preservation in decentralized optimization, in which $N$ agents cooperatively minimize a global objective function that is the sum of $N$ local objective functions. We assume that each local objective function is private and only known to an individual agent. To cooperatively solve the problem, most existing decentralized optimization approaches require participating agents to exchange and disclose estimates to neighboring agents. However, this results in leakage of private information about local objective functions, which is undesirable when adversaries exist and try to steal information from participating agents. To address this issue, we propose a privacy-preserving decentralized optimization approach based on proximal Jacobian ADMM via function decomposition. Numerical simulations confirm the effectiveness of the proposed approach.