将联邦学习与检索增强生成结合，实现隐私保护下的智能问答。
Federated Retrieval-Augmented Generation: A Systematic Mapping Study
- 用联邦学习框架训练模型，数据不出本地。
- 通过外部知识库提升生成内容的准确性和可信度。
- 适合医疗、金融等对隐私要求高的场景使用。
联邦检索增强生成（Federated RAG）融合了联邦学习（FL）与检索增强生成（RAG），在不暴露原始数据的前提下实现分布式模型训练，并通过外部知识提升语言模型的准确性。随着大模型在医疗、金融及个性化服务等隐私敏感领域广泛应用，该方法为安全、知识密集型自然语言处理提供了可行方案。本文首次系统性地梳理了2020至2025年间相关文献，依据证据驱动的软件工程指南，构建了研究焦点、贡献类型与应用领域的结构化分类。分析了架构模式、时间趋势与核心挑战，包括隐私保护下的检索机制、跨客户端异构性以及评估局限性。研究总结了快速发展的领域现状，识别出重复的设计模式，揭示关键开放问题，为未来联邦系统与RAG交叉研究奠定基础。
原文摘要 · Abstract (English)
Federated Retrieval-Augmented Generation (Federated RAG) combines Federated Learning (FL), which enables distributed model training without exposing raw data, with Retrieval-Augmented Generation (RAG), which improves the factual accuracy of language models by grounding outputs in external knowledge. As large language models are increasingly deployed in privacy-sensitive domains such as healthcare, finance, and personalized assistance, Federated RAG offers a promising framework for secure, knowledge-intensive natural language processing (NLP). To the best of our knowledge, this paper presents the first systematic mapping study of Federated RAG, covering literature published between 2020 and 2025. Following Kitchenham's guidelines for evidence-based software engineering, we develop a structured classification of research focuses, contribution types, and application domains. We analyze architectural patterns, temporal trends, and key challenges, including privacy-preserving retrieval, cross-client heterogeneity, and evaluation limitations. Our findings synthesize a rapidly evolving body of research, identify recurring design patterns, and surface open questions, providing a foundation for future work at the intersection of RAG and federated systems.
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