Privacy-Enhancing Paradigms within Federated Multi-Agent Systems

Zitong Shi, Guancheng Wan, Wenke Huang, Guibin Zhang, Jiawei Shao, Mang Ye, Carl Yang

Keywords: Federated Learning, Multi-agent system

Abstract: 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** (EPEAgents), 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 EPEAgents effectively enhances privacy protection while maintaining strong system performance.

Submission Number: 3