Towards privacy-preserving cooperative control via encrypted distributed optimization

TL;DR

This paper introduces a novel encrypted distributed optimization method for privacy-preserving cooperative control in multi-agent systems, enabling secure consensus solutions while protecting local data, demonstrated through a mobile robot formation case study.

Contribution

It proposes a new privacy-preserving cooperative control scheme using encrypted distributed optimization with ADMM, addressing neighbor privacy concerns in multi-agent systems.

Findings

Effective privacy preservation demonstrated in numerical case study

Secure consensus achieved without exposing local data

Applicable to formation control of mobile robots

Abstract

Cooperative control is crucial for the effective operation of dynamical multi-agent systems. Especially for distributed control schemes, it is essential to exchange data between the agents. This becomes a privacy threat if the data is sensitive. Encrypted control has shown the potential to address this risk and ensure confidentiality. However, existing approaches mainly focus on cloud-based control and distributed schemes are restrictive. In this paper, we present a novel privacy-preserving cooperative control scheme based on encrypted distributed optimization. More precisely, we focus on a secure distributed solution of a general consensus problem, which has manifold applications in cooperative control, by means of the alternating direction method of multipliers (ADMM). As a unique feature of our approach, we explicitly take into account the common situation that local decision variables contain copies of quantities associated with neighboring agents and ensure the neighbor's privacy. We show the effectiveness of our method based on a numerical case study dealing with the formation of mobile robots.