面向信息物理系统的分布式共识中信任与韧性的刻画
Characterizing Trust and Resilience in Distributed Consensus for Cyberphysical Systems
AI总结：
本文提出统一数学框架，利用智能体间随机信任信息刻画分布式韧性共识的收敛性、偏差与收敛速率，证明在恶意节点过半时仍可实现几乎必然收敛、指数概率偏差界及有限时间正确分类。
摘要：
本文研究在智能体之间可获得随机信任值的韧性共识问题。具体而言，我们推导出一个统一的数学框架，用于刻画当存在智能体间额外信任信息时的收敛性、共识值相对于真实共识值的偏差以及期望收敛速率。我们证明，在随机信任值和共识协议满足一定条件的情况下：1）即使恶意智能体占网络连接的一半以上，也几乎必然能够收敛到一个共同的极限值；2）收敛极限值相对于无攻击情形（即真实共识值）的偏差可以以趋近于1的指数概率被界定；3）能够在有限时间内几乎必然实现对恶意智能体与合法智能体的正确分类。此外，期望收敛速率随智能体间信任观测质量的提高呈指数衰减。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
