Resilient consensus for multi-agent systems subject to differential privacy requirements
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|Title:||Resilient consensus for multi-agent systems subject to differential privacy requirements||Authors:||Fiore, Davide; Russo, Giovanni||Permanent link:||http://hdl.handle.net/10197/12285||Date:||Aug-2019||Online since:||2021-06-22T15:33:55Z||Abstract:||We consider multi-agent systems interacting over directed network topologies where a subset of agents is adversary/faulty and where the non-faulty agents have the goal of reaching consensus, while fulfilling a differential privacy requirement on their initial conditions. To address this problem, we develop an update law for the non-faulty agents. Specifically, we propose a modification of the so-called Mean-Subsequence-Reduced (MSR) algorithm, the Differentially Private MSR (DP-MSR) algorithm, and characterize three important properties of the algorithm: correctness, accuracy and differential privacy. We show that if the network topology is -robust, then the algorithm allows the non-faulty agents to reach consensus despite the presence of up to faulty agents and we characterize the accuracy of the algorithm. Furthermore, we also show in two important cases that our distributed algorithm can be tuned to guarantee differential privacy of the initial conditions and the differential privacy requirement is related to the maximum network degree. The results are illustrated via simulations.||Funding Details:||Science Foundation Ireland||Type of material:||Journal Article||Publisher:||Elsevier||Journal:||Automatica||Volume:||106||Start page:||18||End page:||26||Copyright (published version):||2019 Elsevier||Keywords:||Multi-agent systems; Resilient consensus; Differential privacy; Networked control systems||DOI:||10.1016/j.automatica.2019.04.029||Language:||en||Status of Item:||Peer reviewed||This item is made available under a Creative Commons License:||https://creativecommons.org/licenses/by-nc-nd/3.0/ie/|
|Appears in Collections:||Electrical and Electronic Engineering Research Collection|
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