arXiv:2606.15179v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) has emerged as a pivotal technique for improving language models by incorporating external knowledge at inference time. As device-cloud collaborative inference makes it feasible to deploy small language models on edge devices, a new setting arises in which private documents remain on the device and public knowledge resides in the cloud. Privacy and policy constraints often forbid raw document exchange, creating a document-isolated dual-end RAG setting. However, existing methods rely on frequent remote synchron
The proliferation of RAG and small language models demands solutions for privacy-preserving inference, making device-cloud collaboration a critical frontier. This paper addresses a key technical challenge for decentralized AI at the edge.
This work is crucial for enabling secure, private, and efficient deployment of advanced AI applications in sensitive environments, opening new use cases where data cannot leave the device.
The ability to perform RAG without raw document exchange between device and cloud dramatically enhances privacy and shifts design paradigms for distributed AI systems.
- · Edge AI device manufacturers
- · Healthcare sector
- · Financial services sector
- · Privacy-focused AI companies
- · AI companies reliant solely on centralized cloud data
- · Generic RAG solution providers without privacy features
Increased adoption of RAG in highly regulated industries due to enhanced privacy guarantees.
Decentralization of AI inference becomes more viable, potentially shifting power dynamics from large cloud providers to device manufacturers and users.
New business models emerge around federated data access and on-device AI knowledge bases, creating specialized markets for privacy-preserving AI tools.
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Read at arXiv cs.AI