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arXiv Machine Learning By Xiaobao Wang, Renda Han, Zhiliang Xia, Meng Liu, Renxiang Guan, Di Jin

Federated Graph Retrieval-Augmented Generation

Read the original on arXiv Machine Learning →

The paper introduces Federated Graph Retrieval-Augmented Generation (FedGRAG), a framework that extends Graph Retrieval-Augmented Generation to federated settings where knowledge graphs are distributed across multiple clients. FedGRAG tackles the lack of a global graph by using a Federated Knowledge Routing mechanism that identifies relevant clients via lightweight indices, and it protects sensitive local graph data through a controlled-disclosure evidence construction method that abstracts retrieved subgraphs into evidence abstracts for server-side reconstruction. Experiments show that FedGRAG outperforms existing baselines in both retrieval and downstream generation tasks, establishing a new paradigm for deploying GraphRAG in federated environments.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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