SynFedRAG: Federated GraphRAG with Persistent Synthetic Knowledge Graph

Federated learning (FL) enables LLM adaptation over decentralized private data, primarily by transferring knowledge through model parameters or adapters. However, for knowledge-intensive question answering (QA), relying solely on local information can be insufficient when relevant evidence is distributed across multiple clients. Retrieval-augmented generation (RAG) provides a natural mechanism for accessing such non-parametric knowledge rather than relying solely on knowledge encoded in model parameters. Existing RAG and GraphRAG systems typically assume centralized access to the underlying corpus or retrieval knowledge, making them difficult to apply when evidence is distributed across private clients. Recent federated RAG methods relax this by constructing shared retrieval memories from client-derived information. However, cross-client evidence may still need to be accessed remotely at query time, rather than being available as a persistent retrieval resource at each client. To address this gap, we propose SYNFEDRAG, which learns a shared synthetic graph that can be queried locally by every client without contacting other clients at inference time. The server constructs a shared synthetic knowledge graph from condensed graph optimized to approximate the retrieval behavior of clients’ private graphs capturing distributed retrieval knowledge without centralizing raw documents or full local graphs. Clients then jointly read from their private and the shared synthetic knowledge graph, allowing them to access complementary retrieval knowledge beyond what is available in their local corpus. A lightweight graph-guided prompt module maps the retrieved subgraphs into soft prompts for a frozen LLM. During federated refinement, private QA supervision jointly adapts the graph-guided prompt module and continuously refines the shared synthetic knowledge graph over successive federated rounds. Experiments on non-IID decentralized multi-hop QA benchmarks show that SYNFEDRAG consistently improves answer quality and cross-client QA performance over local-only GraphRAG and federated LoRA baselines.