mlsanityData to 14 Oct 2026

C-FedRAG: A Confidential Federated Retrieval-Augmented Generation System

arXiv 2412.13163 ↗First posted 17 Dec 2024cs.DC cs.IRcited by 19

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

Abstract (arXiv)

Organizations seeking to utilize Large Language Models (LLMs) for knowledge querying and analysis often encounter challenges in maintaining an LLM fine-tuned on targeted, up-to-date information that keeps answers relevant and grounded. Retrieval Augmented Generation (RAG) has quickly become a feasible solution for organizations looking to overcome the challenges of maintaining proprietary models and to help reduce LLM hallucinations in their query responses. However, RAG comes with its own issues regarding scaling data pipelines across tiered-access and disparate data sources. In many scenarios, it is necessary to query beyond a single data silo to provide richer and more relevant context for an LLM. Analyzing data sources within and across organizational trust boundaries is often limited by complex data-sharing policies that prohibit centralized data storage, therefore, inhibit the fast and effective setup and scaling of RAG solutions. In this paper, we introduce Confidential Computing (CC) techniques as a solution for secure Federated Retrieval Augmented Generation (FedRAG). Our proposed Confidential FedRAG system (C-FedRAG) enables secure connection and scaling of a RAG workflows across a decentralized network of data providers by ensuring context confidentiality. We also demonstrate how to implement a C-FedRAG system using the NVIDIA FLARE SDK and assess its performance using the MedRAG toolkit and MIRAGE benchmarking dataset.

Key facts. Cited by 19 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 9 of its 25 references that are in the map. First posted on arXiv 17 Dec 2024.

Builds on

9 of 25 references.

References · 9

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2021Unsupervised Dense Information Retrieval with Contrastive Learningcited by 1,216
  3. 2021Retrieval Augmentation Reduces Hallucination in Conversationcited by 692
  4. 2013A Theoretical Analysis of NDCG Type Ranking Measurescited by 119
  5. 2024Enhancing LLM Factual Accuracy with RAG to Counter Hallucinations: A Case Study on Domain-Specific Queries in Private Knowledge-Basescited by 77
  6. 2024BioRAG: A RAG-LLM Framework for Biological Question Reasoningcited by 43
  7. 2024Can LLMs' Tuning Methods Work in Medical Multimodal Domain?cited by 12
  8. 2024GenAI-powered Multi-Agent Paradigm for Smart Urban Mobility: Opportunities and Challenges for Integrating Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) with Intelligent Transportation Systemscited by 9
  9. 2024Privacy-Preserving Decentralized AI with Confidential Computingcited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 7; 2026: 12.

19 papers.

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  1. 2025Privacy-Preserving Federated Embedding Learning for Localized Retrieval-Augmented Generationcited by 8
  2. 2025Talk to Right Specialists: Iterative Routing in Multi-agent Systems for Question Answeringcited by 6
  3. 2025Federated Retrieval-Augmented Generation: A Systematic Mapping Studycited by 6
  4. 2026Towards Secure Retrieval-Augmented Generation: A Comprehensive Review of Threats, Defenses and Benchmarkscited by 4
  5. 2026When Latent Agents Lie: KV-Cache Integrity in Multi-Agent LLM Collaborationcited by 4
  6. 2025Distributed Retrieval-Augmented Generationcited by 2
  7. 2026Securing Retrieval-Augmented Generation: A Taxonomy of Attacks, Defenses, and Future Directionscited by 2
  8. 2026Typed Federated Artifacts for the Agentic Web:Sharing Tool-Routing Knowledge Across Frozen,Heterogeneous LLM Agentscited by 2
  9. 2025Efficient Federated Search for Retrieval-Augmented Generation using Lightweight Routingcited by 1
  10. 2026A Wolf in Sheep's Clothing: Targeted Routing Hijacking in Federated RAGcited by 1
  11. 2025Task-Agnostic Federation over Decentralized Data: Research Landscape and Visionscited by 0
  12. 2025DGRAG: Distributed Graph-based Retrieval-Augmented Generation in Edge-Cloud Systemscited by 0
7 more
  1. 2026Federated Customization of Large Models: Approaches, Experiments, and Insightscited by 0
  2. 2026Synapse: Federated Tool Routing via Typed Compendium Artifactscited by 0
  3. 2026Supercharging Federated Intelligence Retrievalcited by 0
  4. 2026FD-RAG: Federated Dual-System Retrieval-Augmented Generationcited by 0
  5. 2026Agents That Know Too Much: A Data-Centric Survey of Privacy in LLM Agentscited by 0
  6. 2026Beyond Raw Context Transfer: Representation-based Federated Retrieval-Augmented Generationcited by 0
  7. 2026Federated Agent Optimizationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Secure Multifaceted-RAG for Enterprise: Hybrid Knowledge Retrieval with Security Filtering
  2. 2025Privacy-Aware RAG: Secure and Isolated Knowledge Retrieval
  3. 2026Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems
  4. 2024T-RAG: Lessons from the LLM Trenches
  5. 2024Pirates of the RAG: Adaptively Attacking LLMs to Leak Knowledge Bases
  6. 2026Trans-RAG: Query-Centric Vector Transformation for Secure Cross-Organizational Retrieval
  7. 2026FedMosaic: Federated Retrieval-Augmented Generation via Parametric Adapters
  8. 2026Mitigating Database Leakage in RAG Systems with Keyword-Grounded Fact Substitution