边缘设备自建知识图谱，云端仅索引摘要，提升隐私与效率。
DGRAG: Distributed Graph-based Retrieval-Augmented Generation in Edge-Cloud Systems
- 边缘端构建本地知识图谱，云端只索引子图摘要，不接触原始数据。
- 查询先在边缘处理，可信度低时才上云，减少云端调用超70%。
- 适合医疗、金融等敏感数据场景，兼顾准确率与隐私保护。
检索增强生成（RAG）通过引入外部知识提升大模型的事实准确性，但传统集中式RAG需汇聚分布式数据，带来隐私风险，并导致高检索延迟和成本。本文提出DGRAG，一种面向边云协同系统的分布式图驱动RAG框架。每个边缘设备将本地文档组织为知识图谱，并定期向云端上传子图级摘要，实现轻量级全局索引，避免原始数据暴露。推理时，查询首先在边缘端处理；门控机制评估多个本地生成结果的置信度与一致性，决定是否返回本地答案或升级至云端。对于升级查询，云端基于摘要匹配定位相关边缘节点，从中检索支持证据，并由云端大模型生成最终回答。在分布式问答任务上的实验表明，DGRAG持续优于去中心化基线方法，同时显著降低云端负载。
原文摘要 · Abstract (English)
Retrieval-Augmented Generation (RAG) improves factuality by grounding LLMs in external knowledge, yet conventional centralized RAG requires aggregating distributed data, raising privacy risks and incurring high retrieval latency and cost. We present DGRAG, a distributed graph-driven RAG framework for edge-cloud collaborative systems. Each edge device organizes local documents into a knowledge graph and periodically uploads subgraph-level summaries to the cloud for lightweight global indexing without exposing raw data. At inference time, queries are first answered on the edge; a gate mechanism assesses the confidence and consistency of multiple local generations to decide whether to return a local answer or escalate the query. For escalated queries, the cloud performs summary-based matching to identify relevant edges, retrieves supporting evidence from them, and generates the final response with a cloud LLM. Experiments on distributed question answering show that DGRAG consistently outperforms decentralized baselines while substantially reducing cloud overhead.
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