Compression-based Privacy Preservation for Distributed Nash Equilibrium Seeking in Aggregative Games

TL;DR

This paper introduces a stochastic compression algorithm for distributed aggregative games that reduces communication load, preserves privacy through differential privacy guarantees, and maintains convergence accuracy, demonstrated via energy consumption simulations.

Contribution

It proposes a novel compression-based method that ensures privacy and convergence in distributed Nash equilibrium seeking, addressing communication and privacy challenges.

Findings

Algorithm guarantees convergence despite aggressive compression.

Achieves differential privacy via stochastic quantization.

Simulation confirms reduced communication and preserved privacy.

Abstract

This paper explores distributed aggregative games in multi-agent systems. Current methods for finding distributed Nash equilibrium require players to send original messages to their neighbors, leading to communication burden and privacy issues. To jointly address these issues, we propose an algorithm that uses stochastic compression to save communication resources and conceal information through random errors induced by compression. Our theoretical analysis shows that the algorithm guarantees convergence accuracy, even with aggressive compression errors used to protect privacy. We prove that the algorithm achieves differential privacy through a stochastic quantization scheme. Simulation results for energy consumption games support the effectiveness of our approach.