Federated Retrieval-Augmented Generation: A Systematic Mapping Study (2025)

Abstract: 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.

## introduction large language models (llms) are increasingly deployed in domains such as healthcare, finance, and personalized assistance, raising concerns about data privacy, ownership, and regulatory compliance. in response, federated learning (fl) has emerged as a compelling paradigm for training models across distributed clients without exchanging raw data @xcite . in parallel, retrieval-augmented generation (rag) @xcite enhances llms by grounding their * corresponding author outputs in dynamically retrieved external knowledge, reducing hallucinations and improving the accuracy of facts @xcite . fl trains models across clients without sharing raw data, using methods like fedavg @xcite and secure aggregation, widely applied in privacy-sensitive @xcite domains. rag is a hybrid method that combines a text retriever with a generator. before producing a response, the model first retrieves relevant text from an external knowledge base or document store. this process helps reduce hallucinations and improves factual consistency in the generated output. popular implementations include the original rag model @xcite and fusion-in-decoder (fid) @xcite , which demonstrate how integrating retrieval can enhance opendomain question answering. at the intersection of these paradigms lies federated rag, a hybrid approach that enables llms to access distributed knowledge sources in a privacypreserving manner. it combines the strengths of data localization, personalized retrieval, and context-aware generation. conceptually, federated rag builds on earlier federated search methods @xcite , which aggregated results from siloed sources without centralized indexing, and extends them to support complex generative tasks. federated rag is distinctive because federated learning safeguards training-time privacy by restricting data to local silos, while retrievalaugmented generation grounds inference-time outputs in external evidence, reducing hallucinations. their integration enables capabilities such as source attribution, local index maintenance, and dynamic document unlearning that neither paradigm achieves alone. as llms have grown in capability, driven by innovations such as the transformer architecture @xcite , pre-training methods @xcite @xcite , and prompting strategies @xcite @xcite , so has the demand for integrating them with heterogeneous private data contexts. since 2019, the parallel evolution of llms, rag, and fl has laid the groundwork for federated rag to emerge as both a viable and increasingly necessary framework. in this paper, we present the first systematic mapping study of federated rag, following the methodology of evidence-based software engineering proposed by @xcite . our objective is to map how retrievalaugmented generation is being adapted and deployed across federated architectures, and to surface recurring trends, gaps, and design patterns in the literature. to this end, we address the following research questions: these questions structure our analysis of the literature published between 2020 and 2025, allowing us to classify prior work, identify underexplored areas, and provide a foundation for future research at the intersection of privacy, retrieval, and generation. ## methodology we searched the best nlp, ml and security venues (2020-2025) using terms such as "federated learning", "retrieval-augmented generation" and "federated search", including backward references. from 50 papers, 18 met criteria. following kitchenham's @xcite ) method, we coded each by (a) research focus, (b) contribution type, (c) application domain. we also recorded the publication year of each study to observe temporal trends and emerging research fronts. based on this categorization, we developed the unified classification scheme (figure 1 ) to summarize the distribution of studies across focus areas and contribution types. furthermore, this schema informed the design of our architectural taxonomy (figure 2 ), which visually organizes the federated rag landscape by contribution, application, and system goals. ## analysis of recent studies retrieval ranges from naive aggregation to secure encrypted search (e.g., frag @xcite ). generation spans centralized, client-specific, or hybrid (e.g., gpt-fedrec @xcite ). these choices balance scalability, personalization, and privacy (figure 2 ). these architectural choices require balancing scalability, performance, personalization, and privacy. ## research activity and publication trends research in federated retrieval-augmented generation (rag) has accelerated notably in recent years, driven by rising demand for privacy-preserving nlp and the maturation of retrieval-augmented systems. before 2022, federated learning (fl) and rag evolved independently, fl focused on decentralized optimization and privacy, while rag addressed hallucinations in large language models (llms) @xcite . their integration began in 2022-2023 with early architectural proposals such as unims-rag @xcite and fede4rag @xcite , which outlined how to combine distributed training with retrievalbased augmentation. figure 4 shows a sharp rise in 2024, driven by high-stakes llm use (healthcare/finance) and regulatory pressure. industry (e.g., nvidia's c-fedrag @xcite ), adobe's mkp-qa @xcite ) and open-source toolkits signal consolidation around benchmarks like mirage @xcite and feb4rag. alongside this growth, the field is also beginning to consolidate around shared metrics and benchmarks, which provide clearer evaluation protocols and practical entry points for researchers and practitioners. notable examples include the mirage clinical qa benchmark @xcite , the feb4rag federated beir benchmark @xcite , the ragas evaluation toolkit for hallucination and consistency @xcite , and the opensource fedrag toolkit (vector institute). together, these resources mark an inflection point: federated rag research is evolving from isolated system proposals to a field with standardized evaluation pipelines. a comprehensive summary of these toolkits and datasets is provided in appendix 5. ## applications federated rag is particularly suited for domains where data sensitivity, personalization, and access to localized knowledge are paramount. in healthcare, it enables clinical question answering systems that draw on hospital-specific knowledge bases while ensuring that sensitive patient data remains on-premise, complying with strict privacy regulations. in financial services, it facilitates regulation-aware summarization and reporting by incorporating institution-specific documents and compliance guidelines without exposing confidential data externally. education, enterprise knowledge management, and legal technology also present high-impact opportunities. in education, federated rag can power personalized tutoring systems that align with local curricula and student learning profiles. enterprise use cases include internal document retrieval and report generation without requiring the centralization of proprietary con-tent. legal applications range from case-specific document analysis to retrieval-augmented reasoning over firm-confidential corpora. these examples illustrate the broad utility of federated rag in supporting privacy-preserving, context-aware, and domain-specific language generation across distributed settings. ## challenges, gaps and trends in this section, we discuss the overarching challenges, research gaps, and emerging trends for federated rag. ## conclusion federated retrieval-augmented generation (rag) has rapidly moved from concept to practice by pairing privacy-preserving training with knowledgegrounded generation. our systematic mapping of 18 primary studies charts this evolution, classifying architectures, contributions, and domains, and highlighting a 2024 research surge driven by large-scale language-model adoption and regulatory pressure for confidential data handling. where it already helps -early deployments demonstrate clear value in compliance-sensitive settings: c-fedrag answers clinical questions while keeping electronic health-record data in-hospital; fede4rag powers enterprise knowledge assistants without centralizing proprietary documents; and ragroute cuts 75% of redundant cross-silo queries, lowering latency for finance workloads. across these use cases, federated rag improves factual grounding, reduces hallucinations, and upholds data-sovereignty requirements that vanilla rag or centralized llm serving cannot meet. what has been solved and what has not -secure enclaves, homomorphic encryption, and differential-privacy mechanisms now offer end-toend confidentiality; neural routing and probabilistic caching tame bandwidth costs; and federated embedding learning supplies parameter-efficient personalization. yet four hurdles persist: why this mapping study matters -federated retrieval-augmented generation has rapidly progressed from early conceptual proposals to a diverse body of system designs, benchmarks, and evaluation resources. our systematic mapping of 18 primary studies charts this trajectory, identifying common architectural patterns, key challenges, and emerging opportunities across domains such as healthcare, finance, and enterprise knowledge management. in moving beyond description, this paper evolves from a static mapping to a field-shaping foundation: it combines structured analysis with empirical metrics, introduces theoretical perspectives through a unified objective function, and distills implementation guidance into actionable design patterns. together, these contributions provide both researchers and practitioners with a clearer roadmap for advancing federated rag toward scalable, trustworthy, and domain-ready deployment.