Supercharging Federated Intelligence Retrieval
Fuente:
arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866918411016077312 |
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| author | Stripelis, Dimitris Foley, Patrick Naseri, Mohammad Lindskog-Münzing, William Ng, Chong Shen Beutel, Daniel Janes Lane, Nicholas D. |
| author_facet | Stripelis, Dimitris Foley, Patrick Naseri, Mohammad Lindskog-Münzing, William Ng, Chong Shen Beutel, Daniel Janes Lane, Nicholas D. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_25374 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Supercharging Federated Intelligence Retrieval Stripelis, Dimitris Foley, Patrick Naseri, Mohammad Lindskog-Münzing, William Ng, Chong Shen Beutel, Daniel Janes Lane, Nicholas D. Information Retrieval Computation and Language Cryptography and Security Machine Learning 68P20, 68T05, 62M45, 68P25, 68T50, 68T10 H.3.3; I.2.7 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. |
| title | Supercharging Federated Intelligence Retrieval |
| topic | Information Retrieval Computation and Language Cryptography and Security Machine Learning 68P20, 68T05, 62M45, 68P25, 68T50, 68T10 H.3.3; I.2.7 |
| url | https://arxiv.org/abs/2603.25374 |