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

arXiv: https://arxiv.org/abs/2412.13163 · HTML: https://mlsanity.com/paper/2412.13163/

## 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.

## Placed in

- Retrieval-augmented generation / [Privacy and security of RAG](https://mlsanity.com/rag/privacy-and-security-of-rag/)

## Builds on (most cited first, 9 of 9)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Unsupervised Dense Information Retrieval with Contrastive Learning](https://mlsanity.com/paper/2112.09118/) (2021)
- [Retrieval Augmentation Reduces Hallucination in Conversation](https://mlsanity.com/paper/2104.07567/) (2021)
- [A Theoretical Analysis of NDCG Type Ranking Measures](https://mlsanity.com/paper/1304.6480/) (2013)
- [Enhancing LLM Factual Accuracy with RAG to Counter Hallucinations: A Case Study on Domain-Specific Queries in Private Knowledge-Bases](https://mlsanity.com/paper/2403.10446/) (2024)
- [BioRAG: A RAG-LLM Framework for Biological Question Reasoning](https://mlsanity.com/paper/2408.01107/) (2024)
- [Can LLMs' Tuning Methods Work in Medical Multimodal Domain?](https://mlsanity.com/paper/2403.06407/) (2024)
- [GenAI-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 Systems](https://mlsanity.com/paper/2409.00494/) (2024)
- [Privacy-Preserving Decentralized AI with Confidential Computing](https://mlsanity.com/paper/2410.13752/) (2024)

## Built on by (most cited first, 19 of 19)

- [Privacy-Preserving Federated Embedding Learning for Localized Retrieval-Augmented Generation](https://mlsanity.com/paper/2504.19101/) (2025)
- [Talk to Right Specialists: Iterative Routing in Multi-agent Systems for Question Answering](https://mlsanity.com/paper/2501.07813/) (2025)
- [Federated Retrieval-Augmented Generation: A Systematic Mapping Study](https://mlsanity.com/paper/2505.18906/) (2025)
- [Towards Secure Retrieval-Augmented Generation: A Comprehensive Review of Threats, Defenses and Benchmarks](https://mlsanity.com/paper/2603.21654/) (2026)
- [When Latent Agents Lie: KV-Cache Integrity in Multi-Agent LLM Collaboration](https://mlsanity.com/paper/2606.28958/) (2026)
- [Distributed Retrieval-Augmented Generation](https://mlsanity.com/paper/2505.00443/) (2025)
- [Securing Retrieval-Augmented Generation: A Taxonomy of Attacks, Defenses, and Future Directions](https://mlsanity.com/paper/2604.08304/) (2026)
- [Typed Federated Artifacts for the Agentic Web:Sharing Tool-Routing Knowledge Across Frozen,Heterogeneous LLM Agents](https://mlsanity.com/paper/2609.06815/) (2026)
- [Efficient Federated Search for Retrieval-Augmented Generation using Lightweight Routing](https://mlsanity.com/paper/2502.19280/) (2025)
- [A Wolf in Sheep's Clothing: Targeted Routing Hijacking in Federated RAG](https://mlsanity.com/paper/2605.28112/) (2026)
- [Task-Agnostic Federation over Decentralized Data: Research Landscape and Visions](https://mlsanity.com/paper/2503.03140/) (2025)
- [DGRAG: Distributed Graph-based Retrieval-Augmented Generation in Edge-Cloud Systems](https://mlsanity.com/paper/2505.19847/) (2025)
- [Federated Customization of Large Models: Approaches, Experiments, and Insights](https://mlsanity.com/paper/2601.00526/) (2026)
- [Synapse: Federated Tool Routing via Typed Compendium Artifacts](https://mlsanity.com/paper/2602.00911/) (2026)
- [Supercharging Federated Intelligence Retrieval](https://mlsanity.com/paper/2603.25374/) (2026)
- [FD-RAG: Federated Dual-System Retrieval-Augmented Generation](https://mlsanity.com/paper/2605.27432/) (2026)
- [Agents That Know Too Much: A Data-Centric Survey of Privacy in LLM Agents](https://mlsanity.com/paper/2606.26627/) (2026)
- [Beyond Raw Context Transfer: Representation-based Federated Retrieval-Augmented Generation](https://mlsanity.com/paper/2609.22162/) (2026)
- [Federated Agent Optimization](https://mlsanity.com/paper/2610.01195/) (2026)

## 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.