サイバーフィジカルシステムにおける分散合意の信頼性とレジリエンスの特性評価
Characterizing Trust and Resilience in Distributed Consensus for Cyberphysical Systems
エージェント間の確率的な信頼値を利用し、悪意あるエージェントが過半数を占めても合意が収束する条件や誤差の確率的評価、悪意エージェントの有限時間識別を統一的に解析する枠組みを導出した。
著者: Michal Yemini, Angelia Nedić, Andrea Goldsmith, Stephanie Gil
分類: math.OC, cs.RO, cs.SY, eess.SP, eess.SY
原文アブストラクト
This work considers the problem of resilient consensus where stochastic values of trust between agents are available. Specifically, we derive a unified mathematical framework to characterize convergence, deviation of the consensus from the true consensus value, and expected convergence rate, when there exists additional information of trust between agents. We show that under certain conditions on the stochastic trust values and consensus protocol: 1) almost sure convergence to a common limit value is possible even when malicious agents constitute more than half of the network connectivity, 2) the deviation of the converged limit, from the case where there is no attack, i.e., the true consensus value, can be bounded with probability that approaches 1 exponentially, and 3) correct classification of malicious and legitimate agents can be attained in finite time almost surely. Further, the expected convergence rate decays exponentially as a function of the quality of the trust observations between agents.
