Fast Distributed Algorithm for Aggregative Games in Malicious Environment

TL;DR

This paper introduces a fast, resilient distributed algorithm for finding Nash equilibria in aggregative games affected by malicious players, using a novel trustworthiness framework and efficient communication strategies.

Contribution

It proposes a new heterogeneous trustworthiness probabilistic framework combined with a multi-round communication mechanism for resilient Nash equilibrium seeking in malicious environments.

Findings

The algorithm converges reliably under malicious conditions.

Simulations demonstrate fast convergence and resilience.

The framework effectively identifies trustworthy players.

Abstract

This paper addresses the distributed Nash Equilibrium seeking problem for aggregative games, where legitimate players' decisions are affected by potential malicious players. To describe players' behavior, we introduce a novel heterogeneous trustworthiness probabilistic framework by employing stochastic trust observations. To mitigate the waste of communication and gradient computation, we utilize a compressible unbalanced network information matrix and a multi-round communication mechanism to develop a fast Nash equilibrium seeking algorithm for aggregative games with unbalanced directed networks. By integrating the multi-round communication mechanism and a trustworthiness broadcast mechanism, we embed our fast convergence algorithm into the heterogeneous trustworthiness probabilistic framework, yielding a resilient fast Nash equilibrium seeking algorithm. Theoretical analysis confirms the convergence of the algorithm. Comparative simulations verify the accuracy of our fast convergence algorithm, and validation simulations verify the resilience of the algorithm.