Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Supercharging Federated Intelligence Retrieval

Dimitris Stripelis, Patrick Foley, Mohammad Naseri, William Lindskog-Münzing, Chong Shen Ng, Daniel Janes Beutel, Nicholas D. Lane

Abstract

RAG typically assumes centralized access to documents, which breaks down when knowledge is distributed across private data silos. We propose a secure Federated RAG system built using Flower that performs local silo retrieval, while server-side aggregation and text generation run inside an attested, confidential compute environment, enabling confidential remote LLM inference even in the presence of honest-but-curious or compromised servers. We also propose a cascading inference approach that incorporates a non-confidential third-party model (e.g., Amazon Nova) as auxiliary context without weakening confidentiality.

Supercharging Federated Intelligence Retrieval

Abstract

RAG typically assumes centralized access to documents, which breaks down when knowledge is distributed across private data silos. We propose a secure Federated RAG system built using Flower that performs local silo retrieval, while server-side aggregation and text generation run inside an attested, confidential compute environment, enabling confidential remote LLM inference even in the presence of honest-but-curious or compromised servers. We also propose a cascading inference approach that incorporates a non-confidential third-party model (e.g., Amazon Nova) as auxiliary context without weakening confidentiality.

Paper Structure

This paper contains 7 sections, 1 figure, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work & Background
  3. Methods
  4. Evaluation
  5. Conclusion
  6. Corpus Specifications
  7. Question & Prompt Sample

Figures (1)

  • Figure 1: FedRAG with confidential server-side aggregation and various inference options.