HyFedRAG: A Federated Retrieval-Augmented Generation Framework for Heterogeneous and Privacy-Sensitive Data

• URL: http://arxiv.org/abs/2509.06444v1
• Date: Mon, 08 Sep 2025 08:44:24 GMT
• Title: HyFedRAG: A Federated Retrieval-Augmented Generation Framework for Heterogeneous and Privacy-Sensitive Data
• Authors: Cheng Qian, Hainan Zhang, Yongxin Tong, Hong-Wei Zheng, Zhiming Zheng,
• Abstract summary: HyFedRAG is a unified and efficient Federated RAG framework tailored for Hybrid data modalities.<n>By leveraging an edge-cloud collaborative mechanism, HyFedRAG enables RAG to operate across diverse data sources while preserving data privacy.
• Score: 16.102325291252324
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Centralized RAG pipelines struggle with heterogeneous and privacy-sensitive data, especially in distributed healthcare settings where patient data spans SQL, knowledge graphs, and clinical notes. Clinicians face difficulties retrieving rare disease cases due to privacy constraints and the limitations of traditional cloud-based RAG systems in handling diverse formats and edge devices. To address this, we introduce HyFedRAG, a unified and efficient Federated RAG framework tailored for Hybrid data modalities. By leveraging an edge-cloud collaborative mechanism, HyFedRAG enables RAG to operate across diverse data sources while preserving data privacy. Our key contributions are: (1) We design an edge-cloud collaborative RAG framework built on Flower, which supports querying structured SQL data, semi-structured knowledge graphs, and unstructured documents. The edge-side LLMs convert diverse data into standardized privacy-preserving representations, and the server-side LLMs integrates them for global reasoning and generation. (2) We integrate lightweight local retrievers with privacy-aware LLMs and provide three anonymization tools that enable each client to produce semantically rich, de-identified summaries for global inference across devices. (3) To optimize response latency and reduce redundant computation, we design a three-tier caching strategy consisting of local cache, intermediate representation cache, and cloud inference cache. Experimental results on PMC-Patients demonstrate that HyFedRAG outperforms existing baselines in terms of retrieval quality, generation consistency, and system efficiency. Our framework offers a scalable and privacy-compliant solution for RAG over structural-heterogeneous data, unlocking the potential of LLMs in sensitive and diverse data environments.

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