Efficient and Privacy Aware Edge Cloud Collaborative Inference for Large Language Models
On-device LLM inference faces a trilemma of response latency, limited hardware resources and user privacy. Full cloud inference delivers strong computing power but exposes user prompts and dialogue data, while standalone on-device inference is unfeasible for most consumer and embedded edge devices. This paper presents a privacy-centric edge-cloud collaborative LLM inference framework built on endpoint-authenticated KV cache. Local endpoints handle input preprocessing, embedding computation, adaptive feature optimization, KV cache authentication, speculative decoding and low-dimensional model head calculation, while the cloud conducts authenticated decoder inference, KV cache management, t...
| Authors | Cheng Li, Jiexiong Liu, Yixuan Chen et al. |
|---|---|
| Primary category | cs.CR |
| Published | 14 July 2026 |
| Last updated at source | 23 July 2026 |
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