Consensus Computation in Unreliable Networks: A System Theoretic Approach (1007.2738v4)

Abstract: This work addresses the problem of ensuring trustworthy computation in a linear consensus network. A solution to this problem is relevant for several tasks in multi-agent systems including motion coordination, clock synchronization, and cooperative estimation. In a linear consensus network, we allow for the presence of misbehaving agents, whose behavior deviate from the nominal consensus evolution. We model misbehaviors as unknown and unmeasurable inputs affecting the network, and we cast the misbehavior detection and identification problem into an unknown-input system theoretic framework. We consider two extreme cases of misbehaving agents, namely faulty (non-colluding) and malicious (Byzantine) agents. First, we characterize the set of inputs that allow misbehaving agents to affect the consensus network while remaining undetected and/or unidentified from certain observing agents. Second, we provide worst-case bounds for the number of concurrent faulty or malicious agents that can be detected and identified. Precisely, the consensus network needs to be 2k+1 (resp. k+1) connected for k malicious (resp. faulty) agents to be generically detectable and identifiable by every well behaving agent. Third, we quantify the effect of undetectable inputs on the final consensus value. Fourth, we design three algorithms to detect and identify misbehaving agents. The first and the second algorithm apply fault detection techniques, and affords complete detection and identification if global knowledge of the network is available to each agent, at a high computational cost. The third algorithm is designed to exploit the presence in the network of weakly interconnected subparts, and provides local detection and identification of misbehaving agents whose behavior deviates more than a threshold, which is quantified in terms of the interconnection structure.

• The paper models misbehaving agents as unknown inputs, distinguishing between Byzantine and faulty agents to analyze their impact on consensus.
• It establishes that a network must be 2k+1 connected to detect k malicious agents, setting critical connectivity thresholds for robust detection.
• Three detection algorithms are proposed that combine global and local approaches to enhance resilience in multi-agent systems under adversarial conditions.

Consensus Computation in Unreliable Networks: A System Theoretic Approach

The paper by Pasqualetti, Bicchi, and Bullo presents a formal analysis of consensus computation in the presence of misbehaving agents within linear consensus networks. The focus is on ensuring reliable computation when some agents in the network may deviate from expected behavior due to faults or malicious intent. This paper is significant for applications in multi-agent systems like motion coordination and clock synchronization.

Key Contributions

1. Modeling Misbehavior: The authors model misbehaving agents as unknown inputs to the network, using a system-theoretic framework. They distinguish between faulty (non-colluding) agents and malicious (Byzantine) agents, providing a nuanced view of potential disruptions.
2. Detection and Identification: The paper explores the detectability and identifiability of these agents. It establishes that the network must be $2k+1$ connected to detect $k$ malicious agents and $k+1$ connected to detect $k$ faulty agents. This finding is critical for designing networks resilient to various types of failures.
3. Effect on Consensus Value: The research quantifies the impact of undetectable inputs on the final consensus value. The authors show that a small change by a misbehaving agent can have significant effects unless the network is robustly designed.
4. Algorithm Design: Three algorithms are proposed for detecting and identifying misbehaving agents:
   • Two algorithms incorporate fault detection techniques, assuming global network knowledge is available.
   • A third, computationally efficient algorithm focuses on local detection and identification by exploiting weakly interconnected subnetwork structures.

Implications and Future Directions

The implications of this research are both theoretical and practical. Theoretically, it contributes to understanding the limits of consensus protocols under adversarial conditions, which is a long-standing problem in distributed systems. Practically, this work guides the design of resilient multi-agent systems that maintain functionality even when some components fail or behave maliciously.

Future research can explore adaptive algorithms that can dynamically adjust network parameters for enhanced resilience. Additionally, applying this system-theoretic approach to different network topologies and communication constraints in real-time scenarios remains an open area of investigation.

Conclusion

This paper rigorously addresses the challenges of consensus computation in networks with unreliable agents. By providing a clear framework for detection and identification, it offers valuable insights into maintaining robust distributed systems. As networked systems become more integral to various domains, these findings can inform the design and implementation of secure and efficient multi-agent protocols.