CRII: CSR: Adaptive Federated Continuous Learning on Heterogeneous Edge Devices with Unlabeled Data

With the growing success of artificial intelligence (AI) techniques, especially deep neural networks (DNNs), there has been an ongoing push to introduce AI services across different domains, including healthcare, autonomous driving, image processing, and more. Due to data privacy issues and unlabeled client datasets, AI service providers must often collect and label their own datasets, and then train their models offline prior to deployment.

However, these pre-trained DNNs may not capture new patterns of online data; they must typically be retrained on user-supplied data. This introduces several challenges: (1) Data Privacy: Users are increasingly concerned about unauthorized access to their private data. (2) Unlabeled Heterogeneous Data: Edge devices are typically deployed in diverse environments and owned by a range of users, leading to substantial variation in the distribution of local data.

Users may also lack the motivation and/or expertise to adequately label their data. (3) Device Heterogeneity: Edge devices exhibit a wide spectrum of computing and memory capabilities, and retraining DNN models on such heterogeneous edge devices can be time-consuming. To overcome these challenges, this project proposes an adaptive, federated, continuous learning system, which uses a novel federated, semi-supervised learning framework to retrain DNN models on distributed, unlabeled, heterogeneous data, while leveraging explainable AI techniques to expedite local training.

This project holds promise for improving AI service adaptation in real-world scenarios by bolstering privacy, adaptability, and efficiency. It supports seamless integration of AI services across diverse domains, while ensuring data privacy and optimizing model performance on edge/client devices. This project also contains a significant educational component.

It will provide opportunities to involve students from groups underrepresented in computing, fostering diversity and inclusion. This will have a positive impact on these students’ education and careers.

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.