为医疗多智能体系统设计抗拜占庭故障的通信协议，确保消息安全与决策可信。
Byzantine Fault-Tolerant Multi-Agent System for Healthcare: A Gossip Protocol Approach to Secure Medical Message Propagation
- 采用基于八卦协议的去中心化传播，结合密码学验证防篡改。
- 支持最多33%故障节点，共识准确率达100%，防重放攻击。
- 适合高可靠性医疗决策场景，可实时监控网络状态与投票结果。
生成式AI推动了医疗领域复杂多智能体架构的发展，使大型语言模型支持协作式临床决策。然而，在对抗性或不可信环境中，这些分布式系统面临消息完整性和容错性的严峻挑战。本文提出一种专为医疗应用设计的拜占庭容错多智能体系统，融合基于八卦协议的消息传播与密码学验证机制。系统由诊断、治疗规划、应急响应和数据分析等专用智能体组成，通过拜占庭共识协议协调，可容忍n=3f+1总节点中的f个故障节点。采用八卦协议实现去中心化消息分发，以2f+1票达成共识，即使在拜占庭故障下仍能正常运行。实验表明，该方法可成功用数字签名验证医疗消息，通过时间戳防御重放攻击，在最多33%拜占庭节点情况下保持100%共识准确率。系统提供共识轮次、投票统计与网络拓扑的实时可视化，支持透明化监控容错操作。本工作为构建可信、鲁棒的医疗多智能体系统提供了实用框架，支持在不可信环境下的协作医疗决策。
原文摘要 · Abstract (English)
Recent advances in generative AI have enabled sophisticated multi-agent architectures for healthcare, where large language models power collaborative clinical decision-making. However, these distributed systems face critical challenges in ensuring message integrity and fault tolerance when operating in adversarial or untrusted environments.This paper presents a novel Byzantine fault-tolerant multi-agent system specifically designed for healthcare applications, integrating gossip-based message propagation with cryptographic validation mechanisms. Our system employs specialized AI agents for diagnosis, treatment planning, emergency response, and data analysis, coordinated through a Byzantine consensus protocol that tolerates up to f faulty nodes among n = 3f + 1 total nodes. We implement a gossip protocol for decentralized message dissemination, achieving consensus with 2f + 1 votes while maintaining system operation even under Byzantine failures. Experimental results demonstrate that our approach successfully validates medical messages with cryptographic signatures, prevents replay attacks through timestamp validation, and maintains consensus accuracy of 100% with up to 33% Byzantine nodes. The system provides real-time visualization of consensus rounds, vote tallies, and network topology, enabling transparent monitoring of fault-tolerant operations. This work contributes a practical framework for building secure, resilient healthcare multi-agent systems capable of collaborative medical decision-making in untrusted environments.
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