CRII: CSR: Enabling On-Device Continual Learning through Enhancing Efficiency of Computing, Memory, and Data

As the number of edge devices (e.g., smartphones, Internet of Things devices) expands, there is a growing demand for intelligent, personalized experiences driven by on-device artificial intelligence (AI) algorithms. This presents challenges for on-device AI to continually learn new knowledge without forgetting prior learnt knowledge. To address this, continual learning (CL) aims to build AI models that are incrementally updated over a sequence of tasks without forgetting. However, when CL applications are deployed on resource-limited edge devices, learning efficiency can suffer, and this is rarely explored in prior work. This project seeks to build an on-device CL framework that will allow continual on-device learning based on local data without forgetting prior learnt knowledge, while enhancing the efficiency of compute, memory, and data use. Specifically, the project will pursue (1) an efficient on-device CL framework that avoids catastrophic forgetting; and (2) extensions that address practical deployment requirements, including limited training data availability and limited labeling in large datasets. Exploring these two cases enhances the applicability of the proposed approach. This project promotes personalized, intelligent user experiences powered by AI, while preserving data privacy and adapting to new information in real-time. It will allow AI models to learn from new data incrementally, reducing the need for large, centralized datasets, thereby promoting privacy. Project results will be explored in the context of automated mobility services for healthcare, and further spur a new line of thinking for a variety of emerging applications of on-device CL, including cost-effective autonomous driving and smart city applications. The project will also yield an educational platform, offering a user-friendly learning tool to support educational goals for K-12 students, undergraduates, graduates, and students from groups underrepresented in computing. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.