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Research Spending & Results

Award Detail

Awardee:GEORGE MASON UNIVERSITY
Doing Business As Name:George Mason University
PD/PI:
  • Cong Wang
  • (703) 993-6755
  • cwang51@gmu.edu
Award Date:09/21/2021
Estimated Total Award Amount: $ 470,023
Funds Obligated to Date: $ 181,584
  • FY 2021=$181,584
Start Date:09/01/2021
End Date:02/28/2026
Transaction Type:Grant
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.070
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:CAREER: Memory-Efficient, Heterogeneity-Aware and Robust Architecture for Federated Intelligence on Edge Devices
Federal Award ID Number:2152580
DUNS ID:077817450
Parent DUNS ID:077817450
Program:CSR-Computer Systems Research
Program Officer:
  • Alexander Jones
  • (703) 292-8950
  • alejones@nsf.gov

Awardee Location

Street:4400 UNIVERSITY DR
City:FAIRFAX
State:VA
ZIP:22030-4422
County:Fairfax
Country:US
Awardee Cong. District:11

Primary Place of Performance

Organization Name:George Mason University
Street:4400 University Drive
City:Fairfax
State:VA
ZIP:22030-4422
County:Fairfax
Country:US
Cong. District:11

Abstract at Time of Award

The recent trend of migrating computation from the centralized cloud to distributed edge devices is reshaping the landscape of today’s Internet, especially under the unprecedented challenges of the COVID19 pandemic. With privacy being a critical concern in data aggregation, Federated Learning emerges as a promising solution to such privacy-utility challenge. It pushes the computation towards consumer’s edge devices, where the data is generated. By exchanging statistical information, the participants perform collaborative learning in a distributed fashion. Unfortunately, the original design still faces new system-architectural challenges from limited memory, software/hardware heterogeneity, security and statistical diversity from different 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 consumer’s 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; 3) secure the information exchange and the machine learning backend. The research will provide a stack of solutions to address the urging 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 consumer’s edge devices and the rising interest of utilizing such private data to improve our wellbeing. The algorithms and tools developed in this CAREER 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, junior research symposium, high school instructor mentorship, coding competitions and the inclusion of underrepresented minority and women engineers. The potential use cases will be also explored with the collaborating industrial partners to enrich their business models. 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.

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