Trustworthy Distributed Average Consensus based on Locally Assessed Trust Evaluations

TL;DR

This paper introduces a distributed consensus algorithm that ensures trustworthy average computation in multi-agent systems despite malicious nodes, by leveraging local trust evaluations and adaptive message filtering.

Contribution

It presents a novel algorithm that incorporates local trust assessments to achieve asymptotic convergence to a trustworthy average in the presence of malicious agents.

Findings

Algorithm guarantees convergence if trust assessments are eventually accurate.

Trust-based filtering prevents malicious influence on the consensus.

Enhanced with two-hop neighbor trust evaluation for robustness.

Abstract

This paper proposes a distributed algorithm for average consensus in a multi-agent system under a fixed bidirectional communication topology, in the presence of malicious agents (nodes) that may try to influence the average consensus outcome by manipulating their updates. The proposed algorithm converges asymptotically to the average of the initial values of the non-malicious nodes, which we refer to as the trustworthy average, as long as the underlying topology that describes the information exchange among the non-malicious nodes is connected. We first present a distributed iterative algorithm that assumes that each node receives (at each iteration or periodically) side information about the trustworthiness of the other nodes, and it uses such trust assessments to determine whether or not to incorporate messages received from its neighbors, as well as to make proper adjustments in its calculation depending on whether a previously trustworthy neighbor becomes untrustworthy or vice-versa. We show that, as long as the trust assessments for each non-malicious node eventually reflect correctly the status (malicious or non-malicious) of its neighboring nodes, the algorithm guarantees asymptotic convergence to the trustworthy average. We subsequently discuss how the proposed algorithm can be enhanced with functionality that enables each node to obtain trust assessments about its neighbors by utilizing information that it receives from its two-hop neighbors at infrequent, perhaps randomly chosen, time instants.