SPEAR: Privacy- and Executability-Aware Subquery Routing for Edge-Cloud Collaborative RAG

Retrieval-augmented generation (RAG) in financial, medical, and enterprise applications often depends on private local data, creating a structural mismatch: edge devices can access these data but have limited reasoning capabilities, while powerful cloud models cannot directly access them. Existing edge-cloud methods largely provide coarse-grained sketches or guidance, limiting their effectiveness on multi-step queries involving mixed public-private knowledge. We propose SPEAR, a subquery-level, privacy- and executability-aware routing method for edge-cloud collaborative RAG. SPEAR desensitizes the user query for cloud-side decomposition into a dependency-structured subquery graph, then jointly estimates each subquery's privacy dependency and edge executability to select its execution site. Subqueries are executed collaboratively according to graph dependencies, and their outputs are fused at the edge. Experiments across multiple privacy-sensitive domain datasets show that SPEAR consistently improves generation quality while limiting private knowledge exposure. Code is available at https://anonymous.4open.science/r/SPEAR-5D6D.