Average consensus with resilience and privacy guarantees without losing accuracy

This paper addresses the challenge of achieving private and resilient average consensus among a group of discrete-time networked agents without compromising accuracy. State-of-the-art solutions to attain privacy and resilient consensus entail an explicit trade-off between the two with an implicit compromise on accuracy. In contrast, in the present work, we propose a methodology that avoids trade-offs between privacy, resilience, and accuracy. We design a methodology that, under certain conditions, enables non-faulty agents, i.e., agents complying with the established protocol, to reach average consensus in the presence of faulty agents, while keeping the non-faulty agents' initial states private. For privacy, agents strategically add noise to obscure their original state, while later withdrawing a function of it to ensure accuracy. Besides, and unlikely many consensus methods, our approach does not require each agent to compute the left-eigenvector of the dynamics matrix associated with the eigenvalue one. Moreover, the proposed framework has a polynomial time complexity relative to the number of agents and the maximum quantity of faulty agents. Finally, we illustrate our method with examples covering diverse faulty agents scenarios.