Privacy‐Preserving Agentic AR : A Fully Localized Edge‐ AI Architecture for Consumer Smart Glasses

ABSTRACT Consumer AR smart glasses are evolving from passive displays into proactive, intelligent assistants. Currently, these ‘Agentic AR’ systems rely heavily on cloud‐based AI. They continuously upload First‐Person View (FPV) video streams and multimodal sensor data. However, this massive data offloading creates severe problems. It introduces critical privacy vulnerabilities, unacceptable network latency and major security risks. To address these challenges, this paper reviews state‐of‐the‐art secure and lightweight Edge‐AI technologies for wearable AIoT devices. We propose a ‘Data‐Never‐Leaves‐Device’ architecture. This fully localized approach entirely eliminates cloud dependency. Our system synergizes three key components: optimized native inference frameworks (e.g., NCNN), ultra‐lightweight perception networks (e.g., YOLO26) and compressed on‐device Large Language Models (LLMs). Together, they execute complex cognitive reasoning directly on the edge. Furthermore, we systematically evaluate the necessary architectural trade‐offs. We analyse the balance between model compression, autonomous usability and strict resource constraints like computation and energy. Ultimately, this paper concludes that migrating AI inference to a secure edge is not just a performance upgrade. It is a fundamental requirement for ensuring user privacy, robust security and the sustainable future of spatial computing.