mlsanityData to 14 Oct 2026

Federated Retrieval-Augmented Generation: A Systematic Mapping Study

arXiv 2505.18906 ↗First posted 24 May 2025cs.CL cs.IRcited by 6

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 16 of its 32 references that are in the map. First posted on arXiv 24 May 2025.

Builds on

16 of 32 references.

References · 16

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2020Language Models are Few-Shot Learnerscited by 32,419
  4. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  5. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  6. 2019Advances and Open Problems in Federated Learningcited by 3,046
  7. 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,210
  8. 2020It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learnerscited by 669
  9. 2024Benchmarking Retrieval-Augmented Generation for Medicinecited by 331
  10. 2021Differentially Private Federated Knowledge Graphs Embeddingcited by 45
  11. 2022FedX: Unsupervised Federated Learning with Cross Knowledge Distillationcited by 40
  12. 2024UniMS-RAG: A Unified Multi-source Retrieval-Augmented Generation for Personalized Dialogue Systemscited by 38
4 more
  1. 2024C-FedRAG: A Confidential Federated Retrieval-Augmented Generation Systemcited by 19
  2. 2022DeTrust-FL: Privacy-Preserving Federated Learning in Decentralized Trust Settingcited by 12
  3. 2020Privacy-preserving Decentralized Aggregation for Federated Learningcited by 10
  4. 2025Federated Retrieval Augmented Generation for Multi-Product Question Answeringcited by 10

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 5.

6 papers.

  1. 2025A Decentralized Retrieval Augmented Generation System with Source Reliabilities Secured on Blockchaincited by 1
  2. 2026Synapse: Federated Tool Routing via Typed Compendium Artifactscited by 0
  3. 2026Supercharging Federated Intelligence Retrievalcited by 0
  4. 2026Privacy-Preserving Text Sanitization for Distributed Agents Collaboration via Disentangled Representationscited by 0
  5. 2026Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systemscited by 0
  6. 2026Federated Agent Optimizationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Privacy-Preserving Federated Embedding Learning for Localized Retrieval-Augmented Generation
  2. 2026FedMosaic: Federated Retrieval-Augmented Generation via Parametric Adapters
  3. 2024Federated Learning and RAG Integration: A Scalable Approach for Medical Large Language Models
  4. 2026Beyond Raw Context Transfer: Representation-based Federated Retrieval-Augmented Generation
  5. 2024FeB4RAG: Evaluating Federated Search in the Context of Retrieval Augmented Generation
  6. 2025Efficient Federated Search for Retrieval-Augmented Generation using Lightweight Routing
  7. 2025A Systematic Review of Key Retrieval-Augmented Generation (RAG) Systems: Progress, Gaps, and Future Directions
  8. 2025Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Survey