Lan (Emily) Zhang (ECE) is the principal investigator of a new grant award from the National Science Foundation titled, “CRII: CNT: IoT-aware Federated On-Device Intelligence.” The amount of the grant is 174,967. The project will advance the current understanding of federated learning in practical yet challenging IoT environments, among other outcomes.
Zhang is an assistant professor in the Department of Electrical and Computer Engineering and an affiliated assistant professor in the Department of Computer Science. She is a member of the Institute of Computing and Cybersystems’s (ICC) Center for Cyber-Physical Systems (CPS).
Zhang is looking for highly motivated PhD or MS students who are interested in solving real-world machine learning, cyber-physical systems, wireless communications, and cybersecurity challenges. The position offers full financial support as a teaching or research assistant.
Recent breakthroughs in on-device machine learning bring artificial intelligence (AI) closer to Internet-of-Things (IoT) devices, shifting the IoT paradigm from “connected things” to “connected intelligence.” Given the presence of confidential IoT data and the widespread impact of data breaches, federated learning provides a privacy-preserving solution that enables knowledge sharing by exchanging on-device model updates rather than private IoT data. However, classical federated learning assumes homogeneous participating devices with a wealth of labeled data, which stands at odds with the properties of most IoT devices, such as resource constraints, heterogeneity, and lack of annotation.
To unleash the potential of federated on-device intelligence in IoT systems, this project focuses on two critical yet open problems: (i) federated knowledge sharing across resource-constrained and heterogeneous IoT devices in a data-free manner; and (ii) federated domain adaptation with limited ground truth labeled knowledge under ever-changing IoT environments. The project develops novel and practical approaches to address conflicting goals on accuracy, efficiency, and data dependence at both the knowledge generation and transfer stages. The proposed research will be thoroughly and rigorously evaluated in simulator-driven and real-world testbeds.
This project will advance the current understanding of federated learning in practical yet challenging IoT environments, address challenges to broad participation of federated on-device intelligence, and enable compelling new IoT applications with federated intelligence. The research outcomes of this project will be integrated with existing curriculums and K-12 programs and disseminated through conferences, seminars, and publications to accelerate progress in AI and IoT research. Furthermore, this project will actively involve undergraduate and underrepresented students and improve the presence of underrepresented minorities in computer science and engineering research.