大型語言模型（Large Language Model， LLM）已逐漸成為行動裝置與邊緣智慧服務的重要核心。然而，純雲端部署容易造成資料外送與延遲問題，純端側部署則受限於算力、記憶體與模型能力。拆分式雲邊協作（Split-based Edge Cloud Collaboration）將同一個模型切分為端側前段與雲端後段，並以中間表徵取代原始輸入進行傳輸，提供了兼顧模型能力與資料最小化的研究方向。然而，此架構仍面臨兩項主要挑戰。第一，Prefill 階段需要傳輸大量中間表徵 （Activation），容易增加第一個 Token 產生時間（Time-To-First-Token， TTFT）。第二，中間表徵雖非原始文字，但仍可能洩漏個人可識別資訊（Personally Identifiable Information， PII）。為解決上述拆分式雲邊協作的瓶頸，本研究針對通訊效率與安全性提出兩項設計。通訊方面，本研究透過中間表徵壓縮降低跨邊界傳輸成本，在主要設定下可將每個 token 的傳輸量降低約 85.7%，並在長提示情境中降低約 65.6% 的 TTFT，同時保持接近未壓縮模型的生成品質。安全性方面，本研究進一步分析壓縮後表徵仍可能被線性探測器讀出敏感資訊，因此加入LEACE隱私防禦機制以降低與回答無關的特定概念。實驗結果顯示，該方法可將最差層的敏感資訊探測準確率由約 90.5% 降至 68.5%。整體而言，本研究展示拆分式雲邊協作在 LLM 推論中透過壓縮來達成通訊效率的提升，與透過防禦機制增加安全性，並提出一個可實作的系統設計方向，使其更接近實際部署需求。;Large Language Models (LLMs) are becoming important in mobile and edge intelligence services. However， cloud-only deployment may cause privacy and latency problems， while on-device deployment is limited by computation， memory， and model size. This study investigates split edge-cloud collaboration， where one LLM is divided into an edge-side front part and a cloud-side back part. This design sends intermediate activations instead of raw text， but it still faces two main challenges: high communication cost and possible privacy leakage. To address these problems， this study improves both communication efficiency and activation security. For communication， activation compression reduces the transmitted data per token by about 85.7% and reduces Time-To-First-Token (TTFT) by about 65.6% in a long-prompt setting， while keeping generation quality close to the uncompressed baseline. For security， this study shows that compressed activations may still leak Personally Identifiable Information (PII)， so a privacy refresh mechanism is added to reduce answer-irrelevant sensitive signals. Experimental results show that the worst-layer probing accuracy is reduced from about 90.5% to 68.5%. Overall， this study analyzes the communication and security problems of split edge-cloud collaboration for LLM inference and provides a practical design for more deployable split LLM systems.
