Federated Retrieval-Augmented Generation: A Systematic Mapping Study
It provides a foundational synthesis for researchers working on secure, knowledge-intensive NLP in privacy-sensitive domains such as healthcare and finance, but it is incremental as a review study.
This paper conducted the first systematic mapping study of Federated Retrieval-Augmented Generation (Federated RAG), analyzing literature from 2020 to 2025 to classify research focuses, architectural patterns, and challenges like privacy-preserving retrieval and cross-client heterogeneity.
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.