Federated RAG Architectures for Distributed Enterprise Knowledge Systems

Abstract

Retrieval-augmented generation (RAG) has become a practical pattern for grounding large language models on enterprise data, but most production deployments still assume a largely centralized corpus, uniform access model, and single retrieval stack. That assumption breaks down in real enterprises, where knowledge is fragmented across document repositories, search appliances, data lakes, knowledge graphs, SaaS systems, and regional boundaries. This paper defines federated RAG as an architectural synthesis of RAG, federated search, federated query processing, enterprise knowledge graphs, and policy-aware access control. The goal is not merely to answer questions from more sources, but to do so while preserving source autonomy, respecting fine-grained authorization, containing latency, and maintaining provenance. Building from work on RAG, dense retrieval, late interaction, federated search, query federation, enterprise knowledge graphs, and attribute-based access control, the paper proposes a brokered multi-stage architecture for distributed enterprise knowledge systems. It argues that the strongest enterprise designs are neither dense-only nor centralized-only. Instead, they combine source selection, hybrid sparse-dense retrieval, hierarchical re-ranking, policy enforcement, and evidence fusion. A comparative analysis shows that federated RAG is most valuable when organizations face data sovereignty, business-unit autonomy, heterogeneous schemas, and rapidly changing private corpora. The paper concludes with design patterns, evaluation criteria, and practical trade-offs for deploying fail-safe, cost-aware, and governance-ready enterprise assistants.