Resilient Consensus Protocol for Multi-Agent Systems with Malicious Agents and Uncertain Trust Observations

Główne pojęcia

The proposed resilient consensus protocol integrates trust observations from physical communication channels and a confidence parameter to achieve consensus among legitimate agents despite the presence of malicious agents and uncertainty in trust information.

Streszczenie

The content presents a resilient consensus protocol for multi-agent systems where agents aim to reach consensus despite the presence of malicious agents that communicate misleading information. The key aspects are:

The protocol incorporates trust observations from the physical communication channels to assess the legitimacy of neighboring agents. However, these trust observations are subject to uncertainty and need to be treated carefully.

The protocol introduces a confidence parameter λt that reflects how confident an agent is about the trustworthiness of its neighbors. This parameter is used to weight the contributions from trusted neighbors, allowing the agents to be more cautious in the early iterations when trust information is more uncertain.

Analytical results show that the proposed protocol achieves resilient consensus in the presence of malicious agents. The steady-state deviation from the nominal consensus can be minimized by tuning the confidence parameter λt based on the statistics of the trust observations.

The protocol does not require an initial observation window T0 as in previous works, which simplifies its implementation.

Numerical simulations corroborate the analysis and demonstrate the effectiveness of the proposed approach.

Statystyki

The content does not provide any specific numerical data or metrics to support the key claims. The analysis is primarily theoretical, focusing on establishing analytical guarantees for the proposed resilient consensus protocol.

Cytaty

"The proposed resilient protocol to be implemented by each legitimate agent i ∈L for t ≥0:
xi
t+1 = λtxi
0 + (1 −λt)
X
j∈N i∪{i}
wij(t)xj
t."
"The time-varying parameter λt ∈[0, 1] accounts for how confident agent i feels about the trustworthiness of its neighbors."
"Analytical and numerical results show that (i) our protocol achieves a resilient consensus in the presence of malicious agents and (ii) the steady-state deviation from nominal consensus can be minimized by a suitable choice of the confidence parameter that depends on the statistics of trust observations."

Kluczowe wnioski z

The Role of Confidence for Trust-based Resilient Consensus (Extended Version)

by Luca Ballott... o arxiv.org 04-12-2024

https://arxiv.org/pdf/2404.07838.pdf

Głębsze pytania

How can the proposed resilient consensus protocol be extended to handle time-varying communication networks or more complex agent dynamics beyond scalar states

The proposed resilient consensus protocol can be extended to handle time-varying communication networks by incorporating dynamic weighting factors that adapt to changes in the network topology. This adaptation can be achieved by introducing algorithms that continuously monitor the network structure and update the trust and confidence parameters accordingly. For instance, the weights assigned to neighbors can be adjusted based on real-time feedback from the communication channels, allowing the agents to dynamically respond to variations in connectivity or the presence of new agents. Additionally, the protocol can be enhanced to consider more complex agent dynamics beyond scalar states by incorporating higher-dimensional state spaces and more sophisticated trust models. This extension would involve developing algorithms that can effectively capture and utilize multi-dimensional information to make decisions about the legitimacy of neighbors and adjust the consensus process accordingly.

What are the potential limitations or drawbacks of relying on physical layer trust observations, and how can these be addressed in future work

Relying solely on physical layer trust observations may have potential limitations and drawbacks that need to be addressed in future work. One limitation is the uncertainty and noise inherent in physical channel measurements, which can lead to inaccuracies in trust assessments. To mitigate this, future research could focus on developing robust algorithms that can filter out noise and distinguish between legitimate and malicious transmissions more effectively. Additionally, the reliance on physical layer observations may introduce vulnerabilities to attacks targeting the communication channels themselves. To address this, future work could explore methods to enhance the security and reliability of the physical channels, such as implementing encryption or authentication mechanisms. Furthermore, the scalability of physical layer trust observations may be a challenge in large-scale systems, requiring efficient algorithms and protocols to handle the increased complexity and volume of data.

How can the insights from this work on the role of confidence in resilient consensus be applied to other distributed optimization or control problems involving uncertain or adversarial environments

The insights from this work on the role of confidence in resilient consensus can be applied to other distributed optimization or control problems involving uncertain or adversarial environments by incorporating similar trust and confidence mechanisms. For instance, in distributed optimization problems, agents may need to collaborate to achieve a common objective while facing uncertainties or adversarial behaviors. By integrating confidence parameters that reflect the agents' trust in each other's contributions, the optimization process can be made more resilient to malicious attacks or noisy data. Additionally, in control problems where multiple agents need to coordinate their actions, confidence-based strategies can help ensure robust performance in the presence of uncertainties or disturbances. By adapting the concepts of trust and confidence introduced in this work, researchers can develop innovative solutions for a wide range of distributed systems facing challenging environments.