Retrieval-Augmented Generation (RAG) systems increasingly leverage structured knowledge graphs to enhance factual accuracy and interpretability. However, scaling such systems introduces challenges in routing queries efficiently across partitioned knowledge sources, particularly under memory and bandwidth constraints. We propose ScatterRAG, a lightweight and scalable routing framework for distributed RAG over partitioned knowledge graphs. ScatterRAG employs Bloom filters for compact indexing and fast negative lookups, combined with fuzzy matching to address lexical variation and noisy queries. This approach enables efficient query filtering and routing without centralized control or exhaustive broadcast. Experimental evaluations on the Natural Questions benchmark show that ScatterRAG achieves acceptable memory usage while maintaining scalability in distributed environments. Although its current prototype yields slower inference and lower retrieval accuracy compared to centralized baselines, ScatterRAG demonstrates a practical balance between scalability and resource efficiency, providing a promising foundation for future research on decentralized and efficient RAG architectures.