FD-RAG: Federated Dual-System Retrieval-Augmented Generation
Retrieval-augmented generation (RAG) has emerged as a paradigm for grounding large language models in external knowledge, yet most existing RAG systems assume centralized knowledge access and ample computation. These assumptions break down in edge environments, where knowledge is fragmented across devices, raw data cannot be shared, and repeated LLM calls are prohibitively expensive. We propose FD-RAG, a federated dual-system RAG framework that decouples lightweight memory access from on-demand LLM reasoning for decentralized deployment. Specifically, FD-RAG learns semantic-aware adaptive hypergraphs over local corpora and distills them into compact QA memories. At inference time, it answ...
| Authors | Tianhao Gao, Kai Yang, Yiyang Li |
|---|---|
| Primary category | cs.IR |
| Published | 22 May 2026 |
| Last updated at source | 22 May 2026 |
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