CAREER: Ubiquitous and Time-Critical Federated Learning with Cooperative Mobile Edge Networking

About This Grant

Federated learning (FL) enables Internet-of-Things (IoT) devices at the network edge to collaboratively learn a shared prediction model while keeping all personal data on the device. However, the current cloud-based FL fails to meet the latency requirements of delay-sensitive IoT applications due to the long-distance transmission between IoT devices and the cloud. This project aims to enable ubiquitous and time-critical FL at the wireless edge to support delay-sensitive and data-driven IoT applications. The project will fulfill the needs of many compelling applications with significant economic and societal impacts such as augmented reality, autonomous driving, mobile healthcare, and smart manufacturing. The project’s educational agenda includes outreach to K-12 with educational summer camps for high-school teachers, mentoring undergraduate and graduate students, especially from minority and underrepresented groups, in the research, and disseminating research outcomes to students and industry partners through new course development and seminars. This project develops a novel Federated learning (FL) framework based on cooperative mobile edge networking that can efficiently support learning and decision making on distributed Internet-of-Things (IoT) data with high accuracy, low latency, and guaranteed privacy. Three interconnected research thrusts are investigated in this project: 1) design of novel network-aware learning algorithms under a two-level network structure to ensure efficient and effective model training from decentralized data on IoT devices over wireless edge networks; 2) jointly optimize resource allocation and learning based on deep reinforcement learning to learn an accurate model rapidly under system heterogeneity and resource constraints; 3) develop novel differential privacy techniques to rigorously protect the privacy of personal data on IoT devices while maintaining high model accuracy and reducing communication cost. The proposed research will enable next-generation wireless edge networks that support a plethora of delay-sensitive and data-driven IoT applications. The proposed research will benefit not only the wireless networking but also machine learning research communities by bridging the gap between the evolving mobile computing and networking technologies and rapidly advancing machine learning techniques. 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.