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This paper introduces PAAC, a privacy-aware agentic framework for device-cloud collaboration that uses a decoupled architecture and LLM-driven sanitization to protect sensitive data while maintaining high performance.
This paper explores collaborative intelligence paradigms where distributed Large Language Models work together across devices and clouds to handle resource constraints. It covers vertical device-cloud collaboration, horizontal multi-agent collaboration, routing policies, and open research challenges in scalable and trustworthy cooperative AI.