In this paper, we study resilient distributed diffusion for multi-task estimation in the presence of adversaries where networked agents must estimate distinct but correlated states of interest by processing streaming data. We show that in general diffusion strategies are not resilient to malicious agents that do not adhere to the diffusion-based information processing rules. In particular, by exploiting the adaptive weights used for diffusing information, we develop time-dependent attack models that drive normal agents to converge to states selected by the attacker. We show that an attacker that has complete knowledge of the system can always drive its targeted agents to its desired estimates. Moreover, an attacker that does not have complete knowledge of the system including streaming data of targeted agents or the parameters they use in diffusion algorithms, can still be successful in deploying an attack by approximating the needed information. The attack models can be used for both stationary and non-stationary state estimation.In addition, we present and analyze a resilient distributed diffusion algorithm that is resilient to any data falsification attack in which the number of compromised agents in the local neighborhood of a normal agent is bounded. The proposed algorithm guarantees that all normal agents converge to their true target states if appropriate parameters are selected. We also analyze trade-off between the resilience of distributed diffusion and its performance in terms of steady-state mean-square-deviation (MSD) from the correct estimates. Finally, we evaluate the proposed attack models and resilient distributed diffusion algorithm using stationary and non-stationary multi-target localization.
Trust-Aware Sequential Decision Making and Rollout Planning for Resilient Multi-Robot Systems
School of Engineering and Applied Sciences, Harvard University（哈佛大学工程与应用科学学院）；Whiting School of Engineering at the Johns Hopkins University（约翰斯·霍普金斯大学惠廷工程学院）
Resilient by Design -- Active Inference for Distributed Continuum Intelligence
Department of Computer Systems and Sciences, Stockholm University（斯德哥尔摩大学计算机系统与科学系）；Department of Information Engineering, University of Pisa（比萨大学信息工程系）；Distributed Systems Group, TU Wien（维也纳技术大学分布式系统组）
