Byzantine Fault-Tolerant Multi-Agent System for Healthcare: A Gossip Protocol Approach to Secure Medical Message Propagation

TL;DR

This paper introduces a Byzantine fault-tolerant multi-agent healthcare system using gossip protocols and cryptographic validation to ensure secure, reliable medical message propagation in adversarial environments.

Contribution

It presents a novel integration of gossip-based message dissemination with Byzantine consensus and cryptographic validation tailored for healthcare multi-agent systems.

Findings

Achieves 100% consensus accuracy with up to 33% Byzantine nodes.

Successfully validates messages using cryptographic signatures and timestamps.

Maintains real-time monitoring of consensus and network topology.

Abstract

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.