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arXiv · 2603.25374

Supercharging Federated Intelligence Retrieval

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

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Dimitris Stripelis, Patrick Foley, Mohammad Naseri, William Lindskog-Münzing, Chong Shen Ng, Daniel Janes Beutel, Nicholas D. Lane. 2026-03-26. Supercharging Federated Intelligence Retrieval. https://arxiv.org/abs/2603.25374

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