arXiv:2605.27432v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) has emerged as a paradigm for grounding large language models in external knowledge, yet most existing RAG systems assume centralized knowledge access and ample computation. These assumptions break down in edge environments, where knowledge is fragmented across devices, raw data cannot be shared, and repeated LLM calls are prohibitively expensive. We propose FD-RAG, a federated dual-system RAG framework that decouples lightweight memory access from on-demand LLM reasoning for decentralized deployment. Specif
The proliferation of AI at the edge and the need for privacy-preserving, distributed AI systems are becoming critical as LLM capabilities expand.
This development addresses key challenges in deploying powerful AI models in environments with limited resources and fragmented data, expanding AI's practical reach.
AI systems can now operate more effectively on distributed, privacy-sensitive data, moving computation and intelligence closer to the data source rather than relying solely on centralized infrastructure.
- · Edge device manufacturers
- · Healthcare providers
- · Industrial IoT operators
- · Privacy-focused AI developers
- · Centralized cloud AI providers (for specialized edge use cases)
- · Traditional RAG system developers (without federated capabilities)
Increased adoption of RAG in highly distributed and privacy-sensitive applications.
Reduced reliance on extensive data centralization for advanced AI capabilities, fostering more localized AI solutions.
Enhanced data sovereignty and privacy frameworks, as raw data sharing becomes less necessary for AI benefits.
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Read at arXiv cs.AI