Resilient Consensus Based on Evidence Theory and Weight Correction

In recent years, the security and resilience of distributed algorithms have become a feature of the utmost importance, especially for applications dealing with sensitive data or critical infrastructures. In this paper, we develop a robust weighted distributed consensus algorithm based on agents' reputations. By resorting to Evidence Theory, our algorithm is able to evaluate the reputation of each communication link in the graph and to update it over time, following the evolution of each node's behavior. Moreover, our approach is able to detect the presence of malicious or faulty nodes that vary their propensity to adhere to the correct consensus strategy over time. Finally, the reputation evaluation process is reinforced at each step by a novel weight correction algorithm, which improves the efficacy of recognizing corrupted nodes and is able to reduce their influence on past history. A simulation campaign completes the paper and demonstrates its effectiveness experimentally.