联邦检索增强生成：一项系统性映射研究
Federated Retrieval-Augmented Generation: A Systematic Mapping Study
AI总结：
本文首次开展联邦检索增强生成（Federated RAG）的系统性映射研究，覆盖2020-2025年文献，按循证指南分类研究重点等内容，分析架构、趋势与挑战，为该交叉领域后续研究奠基。
摘要：
联邦检索增强生成（Federated RAG）将联邦学习（Federated Learning, FL，可在不暴露原始数据的前提下实现分布式模型训练）与检索增强生成（Retrieval-Augmented Generation, RAG，通过将输出锚定在外部知识上来提升语言模型的事实准确性）相结合。随着大语言模型越来越多地部署在医疗、金融、个性化助手等隐私敏感领域，Federated RAG为安全的知识密集型自然语言处理（NLP）提供了一个极具前景的框架。据我们所知，本文首次开展了Federated RAG的系统性映射研究，覆盖2020年至2025年间发表的文献。我们遵循Kitchenham的循证软件工程指南，对研究重点、贡献类型和应用领域进行了结构化分类。我们分析了架构模式、时间趋势和关键挑战，包括隐私保护检索、跨客户端异构性和评估局限性。我们的研究结果综合了这一快速发展的研究体系，识别了反复出现的设计模式，并提出了开放性问题，为RAG与联邦系统交叉领域的未来工作奠定了基础。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
