Cong Wang, Assistant Professor, Cyber Security Engineering, received a National Science Foundation CAREER grant for a project on memory-efficient, heterogeneity-aware, and robust architecture for federated intelligence on edge devices.
The overarching goal of this CAREER project is to design, optimize, and implement a memory-efficient, heterogeneity-aware, and robust architecture for federated learning on consumers' edge devices. In particular, it aims to: 1) remove the memory barriers of running the computational-intensive learning tasks; 2) resolve the software and hardware heterogeneity among various kinds of devices; and 3) secure the information exchange and the machine learning backend. The research will provide a stack of solutions to address the urgent needs in realizing collaborative intelligence on edge devices with computation/memory/energy-efficiencies, security, and robustness.
"This research will address an urgent problem to bridge the gap between the vast data available from consumers' edge devices and the rising interest of utilizing such private data to improve our well-being," Wang said.
The algorithms and tools developed in this project will lay the foundations to a plethora of new applications on massively distributed edge devices as the essential elements for building a smart, connected, and resilient community.
The CAREER program will advance STEM education by developing new educational components related to machine learning, edge computing, and security. This includes diverse outreach plans of cybersecurity summer camps, a junior research symposium, high school instructor mentorship, coding competitions, and the inclusion of underrepresented minority and women engineers. The potential use cases will also be explored with the collaborating industrial partners to enrich their business models.
Wang received $181,584 from NSF for this award. Funding began in September 2021 and will end in February 2026.
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