mlsanityData to 14 Oct 2026

Distributed Retrieval-Augmented Generation

arXiv 2505.00443 ↗First posted 1 May 2025cs.DCcited by 2

On the map: Retrieval-augmented generation → Privacy and security of RAG

Abstract (arXiv)

As large language models (LLMs) become increasingly adopted on edge devices, Retrieval-Augmented Generation (RAG) is gaining prominence as a solution to address factual deficiencies and hallucinations by integrating external knowledge. However, centralized RAG architectures face significant challenges in data privacy and scalability. For instance, smart healthcare services often rely on collecting sensitive patient data and building a centralized knowledge base to provide better diagnosis and treatment advice, while privacy concerns significantly impede this process. Besides, maintaining a comprehensive and continuously updated knowledge base is costly, particularly in response to regional epidemics and rapidly mutating viruses. To address these challenges, this paper introduces Distributed Retrieval-Augmented Generation (DRAG), a novel framework that improves data privacy by eliminating the need for a centralized knowledge base and restoring data control to owners. DRAG incorporates a Topic-Aware Random Walk (TARW) algorithm that leverages LLMs to extract query topics and facilitate targeted peer discovery within a peer-to-peer network, enabling efficient knowledge retrieval in decentralized environments. Extensive experiments across three diverse datasets and LLMs demonstrate that DRAG with TARW achieves near-centralized RAG performance by using half as many messages as flooding. The code is available at https://github.com/xuchenhao001/DRAG.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Privacy and security of RAG, one of 15 directions in Retrieval-augmented generation. Builds on 32 of its 38 references that are in the map. First posted on arXiv 1 May 2025.

Builds on

32 of 38 references.

Closest ancestors · 11

Same direction, newest first

  1. 2025RAG-WM: An Efficient Black-Box Watermarking Approach for Retrieval-Augmented Generation of Large Language Models9 Jan
  2. 2024RAG with Differential Privacy26 Dec
  3. 2024C-FedRAG: A Confidential Federated Retrieval-Augmented Generation System17 Dec
  4. 2024Privacy-Preserving Retrieval-Augmented Generation with Differential Privacy6 Dec
  5. 2024Feedback-Guided Extraction of Knowledge Base from Retrieval-Augmented LLM Applications21 Nov
  6. 2024Mask-based Membership Inference Attacks for Retrieval-Augmented Generation26 Oct
  7. 2024FRAG: Toward Federated Vector Database Management for Collaborative and Secure Retrieval-Augmented Generation17 Oct
  8. 2024Mitigating the Privacy Issues in Retrieval-Augmented Generation (RAG) via Pure Synthetic Data20 Jun
  9. 2024BadRAG: Identifying Vulnerabilities in Retrieval Augmented Generation of Large Language Models3 Jun
  10. 2024Is My Data in Your Retrieval Database? Membership Inference Attacks Against Retrieval Augmented Generation30 May
  11. 2024The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG)23 Feb

Other references · 21

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  3. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
  4. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,141
  5. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,839
  6. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarizationcited by 1,453
  7. 2024A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Modelscited by 452
  8. 2023Benchmarking Large Language Models in Retrieval-Augmented Generationcited by 312
  9. 2024RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMscited by 202
  10. 2023Large Language Model Routing with Benchmark Datasetscited by 172
  11. 2024GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoningcited by 128
  12. 2024Mobile Edge Intelligence for Large Language Models: A Contemporary Surveycited by 128
9 more
  1. 2023Vision Language Models in Autonomous Driving: A Survey and Outlookcited by 126
  2. 2024A Review on Edge Large Language Models: Design, Execution, and Applicationscited by 100
  3. 2021Pre-trained Language Models in Biomedical Domain: A Systematic Surveycited by 87
  4. 2024GRAG: Graph Retrieval-Augmented Generationcited by 78
  5. 2025ArchRAG: Attributed Community-based Hierarchical Retrieval-Augmented Generationcited by 28
  6. 2024FeB4RAG: Evaluating Federated Search in the Context of Retrieval Augmented Generationcited by 19
  7. 2024Evaluating the Efficacy of Open-Source LLMs in Enterprise-Specific RAG Systems: A Comparative Study of Performance and Scalabilitycited by 15
  8. 2024Hierarchical Retrieval-Augmented Generation Model with Rethink for Multi-hop Question Answeringcited by 14
  9. 2023Don't forget private retrieval: distributed private similarity search for large language modelscited by 5

Built on by

Cited by year (2026 to 14 Oct)

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

2 papers.

  1. 2025DGRAG: Distributed Graph-based Retrieval-Augmented Generation in Edge-Cloud Systemscited by 0
  2. 2026Beyond Raw Context Transfer: Representation-based Federated Retrieval-Augmented Generationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Pirates of the RAG: Adaptively Attacking LLMs to Leak Knowledge Bases
  2. 2026Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems
  3. 2026SoK: Privacy Risks and Mitigations in Retrieval-Augmented Generation Systems
  4. 2025Secure Multifaceted-RAG for Enterprise: Hybrid Knowledge Retrieval with Security Filtering
  5. 2025DRAG: Distilling RAG for SLMs from LLMs to Transfer Knowledge and Mitigate Hallucination via Evidence and Graph-based Distillation
  6. 2026Benchmarking Knowledge-Extraction Attack and Defense on Retrieval-Augmented Generation
  7. 2025Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Survey
  8. 2025Federated Retrieval-Augmented Generation: A Systematic Mapping Study