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technologyreview.com·2h ago
AI Research
arXiv CS.AI·May 28·8 min read
FD-RAG: Federated Dual-System Retrieval-Augmented Generation
TL;DR
Retrieval-augmented generation (RAG)—a technique where AI models fetch relevant documents before generating answers—faces challenges when data is distributed across multiple private organizations that cannot share raw information. FD-RAG introduces a federated dual-system architecture that enables collaborative RAG without centralizing sensitive data.
✦ Why It Matters
Engineers can now build privacy-compliant RAG systems across organizations without centralizing sensitive data.
Key Takeaways
- Deploy RAG across multiple organizations without sharing raw documents or centralizing data.
- Use federated learning to train retrieval and generation components collaboratively while maintaining privacy.
- Validate privacy-preserving RAG on standard benchmarks before production deployment.
- Separate retrieval and generation into independent systems to enable modular federated architecture.
Full Summary
RAG systems enhance language model outputs by retrieving relevant documents before generation, but deploying RAG across organizations with privacy constraints remains unsolved. FD-RAG addresses this by implementing federated learning—a distributed training approach where models improve without sharing raw data—combined with a dual-system design separating retrieval and generation components.
The architecture allows multiple parties to contribute retrieval indices and generation models while maintaining data privacy through local computation and encrypted communication. The system was evaluated on standard information retrieval and question-answering benchmarks, showing that federated RAG achieves comparable accuracy to centralized baselines while preserving organizational data confidentiality.
This enables enterprises and research institutions to build collaborative AI systems without exposing proprietary documents or user information.