  This demo presents a novel end-to-end framework that combines on-device large
language models (LLMs) with smartphone sensing technologies to achieve
context-aware and personalized services. The framework addresses critical
limitations of current personalization solutions via cloud LLMs, such as
privacy concerns, latency and cost, and limited personal information. To
achieve this, we innovatively proposed deploying LLMs on smartphones with
multimodal sensor data through context-aware sensing and customized prompt
engineering, ensuring privacy and enhancing personalization performance. A case
study involving a university student demonstrated the capability of the
framework to provide tailored recommendations. In addition, we show that the
framework achieves the best trade-off in privacy, performance, latency, cost,
battery and energy consumption between on-device and cloud LLMs. To the best of
our knowledge, this is the first framework to provide on-device LLMs
personalization with smartphone sensing. Future work will incorporate more
diverse sensor data and involve extensive user studies to enhance
personalization. Our proposed framework has the potential to substantially
improve user experiences across domains including healthcare, productivity, and
entertainment.
