Vector-valued Privacy-Preserving Average Consensus

TL;DR

This paper introduces a novel distributed algorithm for vector-valued multi-agent networks that achieves exact average consensus while preserving the privacy of individual initial states, using matrix-weighted networks and low-rank coupling matrices.

Contribution

The paper proposes a new vector-valued privacy-preserving average consensus algorithm based on matrix-weighted networks and dynamic low-rank matrices, improving efficiency and privacy in multi-agent systems.

Findings

Guarantees exact average consensus with privacy preservation

Uses only basic matrix operations, enhancing computational efficiency

Can be implemented fully distributed without third-party reliance

Abstract

Achieving average consensus without disclosing sensitive information can be a critical concern for multi-agent coordination. This paper examines privacy-preserving average consensus (PPAC) for vector-valued multi-agent networks. In particular, a set of agents with vector-valued states aim to collaboratively reach an exact average consensus of their initial states, while each agent's initial state cannot be disclosed to other agents. We show that the vector-valued PPAC problem can be solved via associated matrix-weighted networks with the higher-dimensional agent state. Specifically, a novel distributed vector-valued PPAC algorithm is proposed by lifting the agent-state to higher-dimensional space and designing the associated matrix-weighted network with dynamic, low-rank, positive semi-definite coupling matrices to both conceal the vector-valued agent state and guarantee that the multi-agent network asymptotically converges to the average consensus. Essentially, the convergence analysis can be transformed into the average consensus problem on switching matrix-weighted networks. We show that the exact average consensus can be guaranteed and the initial agents' states can be kept private if each agent has at least one "legitimate" neighbor. The algorithm, involving only basic matrix operations, is computationally more efficient than cryptography-based approaches and can be implemented in a fully distributed manner without relying on a third party. Numerical simulation is provided to illustrate the effectiveness of the proposed algorithm.