LLM-based Multi-Agent Systems (MAS) have proven highly effective in solving complex problems by integrating multiple agents, each performing different roles. However, in sensitive domains, they face emerging privacy protection challenges. In this paper, we introduce the concept of Federated MAS, highlighting the fundamental differences between Federated MAS and traditional FL. We then identify key challenges in developing Federated MAS, including: 1) heterogeneous privacy protocols among agents, 2) structural differences in multi-party conversations, and 3) dynamic conversational network structures. To address these challenges, we propose Embedded Privacy-Enhancing Agents (EPEAgent), an innovative solution that integrates seamlessly into the Retrieval-Augmented Generation (RAG) phase and the context retrieval stage. This solution minimizes data flows, ensuring that only task-relevant, agent-specific information is shared. Additionally, we design and generate a comprehensive dataset to evaluate the proposed paradigm. Extensive experiments demonstrate that EPEAgent effectively enhances privacy protection while maintaining strong system performance. The code will be availiable at https://github.com/ZitongShi/EPEAgent

本研究针对在敏感领域中，现有多智能体系统所面临的隐私保护挑战进行了探讨。论文提出了嵌入式隐私增强智能体（EPEAgent）这一创新解决方案，通过在检索增强生成阶段和上下文检索阶段无缝集成，最小化数据流并确保共享的仅为与任务相关的智能体特定信息，显著提升隐私保护。实验结果表明，EPEAgent在强系统性能的同时有效增强了隐私保护。 

联邦多智能体系统中的隐私增强范式