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

• URL: http://arxiv.org/abs/2412.13163v2
• Date: Wed, 18 Dec 2024 21:26:14 GMT
• Title: C-FedRAG: A Confidential Federated Retrieval-Augmented Generation System
• Authors: Parker Addison, Minh-Tuan H. Nguyen, Tomislav Medan, Jinali Shah, Mohammad T. Manzari, Brendan McElrone, Laksh Lalwani, Aboli More, Smita Sharma, Holger R. Roth, Isaac Yang, Chester Chen, Daguang Xu, Yan Cheng, Andrew Feng, Ziyue Xu,
• Abstract summary: We introduce Confidential Computing (CC) techniques as a solution for secure Federated Retrieval Augmented Generation (FedRAG)<n>Our proposed Confidential FedRAG system (C-FedRAG) enables secure connection and scaling of a RAG across a decentralized network of data providers by ensuring context confidentiality.
• Score: 7.385458207094507
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Privacy-Preserving Federated Embedding Learning for Localized Retrieval-Augmented Generation [60.81109086640437]
  We propose a novel framework called Federated Retrieval-Augmented Generation (FedE4RAG) FedE4RAG facilitates collaborative training of client-side RAG retrieval models. We apply homomorphic encryption within federated learning to safeguard model parameters.
  arXiv  Detail & Related papers  (2025-04-27T04:26:02Z)
• Secure Multifaceted-RAG for Enterprise: Hybrid Knowledge Retrieval with Security Filtering [21.557667589509503]
  Existing Retrieval-Augmented Generation (RAG) systems face challenges in enterprise settings due to limited retrieval scope and data security risks. We propose the Secure Multifaceted-RAG (SecMulti-RAG) framework, which retrieves not only from internal documents but also from two supplementary sources. In our evaluation on a report generation task in the automotive industry, SecMulti-RAG significantly outperforms traditional RAG.
  arXiv  Detail & Related papers  (2025-04-18T02:51:29Z)
• How Robust Are Router-LLMs? Analysis of the Fragility of LLM Routing Capabilities [62.474732677086855]
  Large language model (LLM) routing has emerged as a crucial strategy for balancing computational costs with performance. We propose the DSC benchmark: Diverse, Simple, and Categorized, an evaluation framework that categorizes router performance across a broad spectrum of query types.
  arXiv  Detail & Related papers  (2025-03-20T19:52:30Z)
• MES-RAG: Bringing Multi-modal, Entity-Storage, and Secure Enhancements to RAG [65.0423152595537]
  We propose MES-RAG, which enhances entity-specific query handling and provides accurate, secure, and consistent responses. MES-RAG introduces proactive security measures that ensure system integrity by applying protections prior to data access. Experimental results demonstrate that MES-RAG significantly improves both accuracy and recall, highlighting its effectiveness in advancing the security and utility of question-answering.
  arXiv  Detail & Related papers  (2025-03-17T08:09:42Z)
• Dataset Protection via Watermarked Canaries in Retrieval-Augmented LLMs [67.0310240737424]
  We introduce a novel approach to safeguard the ownership of text datasets and effectively detect unauthorized use by the RA-LLMs. Our approach preserves the original data completely unchanged while protecting it by inserting specifically designed canary documents into the IP dataset. During the detection process, unauthorized usage is identified by querying the canary documents and analyzing the responses of RA-LLMs.
  arXiv  Detail & Related papers  (2025-02-15T04:56:45Z)
• Know Your RAG: Dataset Taxonomy and Generation Strategies for Evaluating RAG Systems [18.62773754004561]
  We show that using public question and answer (Q&A) datasets to assess retrieval performance can lead to non-optimal systems design.<n>We propose solutions based on the characterization of RAG datasets through labels and through label-targeted data generation.
  arXiv  Detail & Related papers  (2024-11-29T13:57:07Z)
• FRAG: Toward Federated Vector Database Management for Collaborative and Secure Retrieval-Augmented Generation [1.3824176915623292]
  This paper introduces textitFederated Retrieval-Augmented Generation (FRAG), a novel database management paradigm tailored for the growing needs of retrieval-augmented generation (RAG) systems. FRAG enables mutually-distrusted parties to collaboratively perform Approximate $k$-Nearest Neighbor (ANN) searches on encrypted query vectors and encrypted data stored in distributed vector databases.
  arXiv  Detail & Related papers  (2024-10-17T06:57:29Z)
• Data Advisor: Dynamic Data Curation for Safety Alignment of Large Language Models [79.65071553905021]
  We propose Data Advisor, a method for generating data that takes into account the characteristics of the desired dataset. Data Advisor monitors the status of the generated data, identifies weaknesses in the current dataset, and advises the next iteration of data generation.
  arXiv  Detail & Related papers  (2024-10-07T17:59:58Z)
• WeKnow-RAG: An Adaptive Approach for Retrieval-Augmented Generation Integrating Web Search and Knowledge Graphs [10.380692079063467]
  We propose WeKnow-RAG, which integrates Web search and Knowledge Graphs into a "Retrieval-Augmented Generation (RAG)" system. First, the accuracy and reliability of LLM responses are improved by combining the structured representation of Knowledge Graphs with the flexibility of dense vector retrieval. Our approach effectively balances the efficiency and accuracy of information retrieval, thus improving the overall retrieval process.
  arXiv  Detail & Related papers  (2024-08-14T15:19:16Z)
• RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework [69.4501863547618]
  This paper introduces RAGEval, a framework designed to assess RAG systems across diverse scenarios. With a focus on factual accuracy, we propose three novel metrics Completeness, Hallucination, and Irrelevance. Experimental results show that RAGEval outperforms zero-shot and one-shot methods in terms of clarity, safety, conformity, and richness of generated samples.
  arXiv  Detail & Related papers  (2024-08-02T13:35:11Z)
• ERATTA: Extreme RAG for Table To Answers with Large Language Models [1.3318204310917532]
  Large language models (LLMs) with retrieval augmented-generation (RAG) have been the optimal choice for scalable generative AI solutions. We propose a unique LLM-based system where multiple LLMs can be invoked to enable data authentication, user-query routing, data-retrieval and custom prompting for question-answering capabilities from Enterprise-data tables. Our proposed system and scoring metrics achieve >90% confidence scores across hundreds of user queries in the sustainability, financial health and social media domains.
  arXiv  Detail & Related papers  (2024-05-07T02:49:59Z)
• The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG) [56.67603627046346]
  Retrieval-augmented generation (RAG) is a powerful technique to facilitate language model with proprietary and private data. In this work, we conduct empirical studies with novel attack methods, which demonstrate the vulnerability of RAG systems on leaking the private retrieval database.
  arXiv  Detail & Related papers  (2024-02-23T18:35:15Z)
• PS-FedGAN: An Efficient Federated Learning Framework Based on Partially Shared Generative Adversarial Networks For Data Privacy [56.347786940414935]
  Federated Learning (FL) has emerged as an effective learning paradigm for distributed computation. This work proposes a novel FL framework that requires only partial GAN model sharing. Named as PS-FedGAN, this new framework enhances the GAN releasing and training mechanism to address heterogeneous data distributions.
  arXiv  Detail & Related papers  (2023-05-19T05:39:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.