Project Grant 2142987

The National Science Foundation Division of Information and Intelligent Systems awarded Duke University a $150,000 Project Grant under the Computer and Information Science and Engineering program (CFDA 47.070) for the period of September 1, 2021 through August 31, 2023. The grant funds the "EAGER: Distributed Heterogeneous Data Analytics via Federated Learning" project. This project supports the development of federated learning techniques to enable distributed and privacy-preserving...

This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) provides $199,999 to San Francisco State University to develop a resilient next-generation (NextG) network design for federated learning over mobile devices. The key products and services to be delivered under this 2-year award include: Exploiting serverless computing at the network edge to efficiently provide machine learning computing...

This $348,573 project grant from the National Science Foundation's Division of Electrical, Communications and Cyber Systems, under the Engineering federal grant program (CFDA 47.041), will fund research at North Carolina State University from September 2022 through August 2025. The university will advance the frontiers of federated learning through exploring tradeoffs among learning performance, communication efficiency, privacy protection, and system robustness under a generalized...

This federal Project Grant, awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program, aims to design and develop a secure and efficient decentralized federated learning (DFL) system. The $380,667 grant, awarded on August 15, 2024, will fund research to address communication, computation, and security issues in DFL, which enables training of data-hungry machine learning models on local devices without sharing raw data. The...

This $219,332 Project Grant awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) Federal Grant Program aims to develop a secure and efficient decentralized federated learning (DFL) system. The key products and services to be delivered include: Developing computational theories, models, and prototype systems to establish the foundations for trustworthy DFL, addressing both high-performance accuracy and security with privacy...

Virginia Polytechnic Institute & State University (Virginia Tech) was awarded a $165,200 project grant from the National Science Foundation's (NSF) Computer and Information Science and Engineering program (CFDA 47.070) to support research titled "COLLABORATIVE RESEARCH: CNS CORE: SMALL: HIERARCHICAL FEDERATED LEARNING OVER WIRELESS EDGE NETWORKS: PERFORMANCE ANALYSIS AND OPTIMIZATION." Under this three-year award beginning November 1, 2021, Virginia Tech researchers will analyze...

This Project Grant award for $225,000 from the National Science Foundation (NSF) Engineering program (CFDA 47.041) is funding research by the University of Florida on hierarchical federated learning (HFL) over highly dense and overlapping next-generation (NextG) wireless network deployments. The key objectives are to: (i) design short-term bandwidth allocation for HFL under dense and heterogeneous deployments, (ii) develop a long-term optimization framework to solve user selection and...

This $173,754 Project Grant awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) aims to develop an innovative privacy-preserving federated learning (FL) framework suitable for heterogeneous edge devices. The key objectives are to: 1) enable tailored device-specific models to mitigate biases and enhance performance across diverse computational capabilities and data distributions, 2) utilize differential privacy...

The National Science Foundation (NSF) Division of Computing and Communication Foundations awarded a $150,000 Project Grant to The Leland Stanford Junior University (Stanford University) to support the "COLLABORATIVE RESEARCH: SHF: SMALL: QUASI WEIGHTLESS NEURAL NETWORKS FOR ENERGY-EFFICIENT MACHINE LEARNING ON THE EDGE" project under the NSF's Computer and Information Science and Engineering (CFDA 47.070) program. The project aims to develop low-energy machine learning hardware that...

This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Project Grant award, with a total funding of $174,770, supports the development of an adaptive, federated, continuous learning system that uses a novel federated, semi-supervised learning framework. This framework aims to retrain deep neural network models on distributed, unlabeled, heterogeneous data from edge devices, while leveraging explainable AI techniques to expedite local training. The...