Project Grant 2315613
Award Date
10/1/23
Completion Date
9/30/25
Dollars Obligated
$130K
Funding Federal Agency
Division of Information and Intelligent Systems
Awarding Federal Agency
Division of Computer and Network Systems
Federal Grant Program
47.070
Assistance Type
Project Grant
Place of Performance
Newark, DE 19716, USA
Similar Awards
This National Science Foundation (NSF) Project Grant award under the Computer and Information Science and Engineering program (CFDA 47.070) provides $170,000 to The Research Foundation For The State University Of New York, doing business as Stony Brook University, from October 1, 2023 to September 30, 2025. The project aims to conduct a comprehensive analysis of the characteristics and vulnerabilities of critical learning periods in federated learning systems in order to advance the study of the...
This National Science Foundation project grant of $599,999 will fund research at Duke University from October 2022 through September 2026 towards developing secure methods for federated learning. Federated learning is an emerging machine learning technique that allows analysis of private data without centralized collection, but current methods lack security protections. Under the Computer and Information Science and Engineering program (CFDA 47.070), the researchers will explore new security...
This four-year $300,000 Project Grant from the National Science Foundation's Computer and Information Science and Engineering program aims to develop secure foundations for federated learning. Federated learning enables machine learning models to be collaboratively trained using data from many client devices without sharing private information. The researchers will investigate security vulnerabilities in federated learning's training phase, such as poisoning and backdoor attacks. They will...
This $196,548 federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will support Temple University's research to develop a new Decentralized Federated Compositional Learning (DFCL) framework. The objective is to enable federated learning, an emerging collaborative machine learning paradigm, for the compositional learning paradigm. The project aims to address key computational and communication...
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 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...
The National Science Foundation awarded a $300,000 project grant to Princeton University under the Computer and Information Science and Engineering program to support research towards securing federated learning. Over a four-year period ending September 2026, Princeton researchers will investigate security vulnerabilities in the training phase of federated learning models, develop provably secure federated learning methods to prevent poisoning and backdoor attacks, and create techniques to...
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...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Program (CFDA 47.070) project grant, awarded to North Carolina State University, is focused on enhancing privacy in federated learning, which is an AI approach enabling knowledge sharing without compromising data privacy. The $220,258 grant, awarded on October 1, 2024, aims to address vulnerabilities in federated learning schemes that may leak sensitive information through improper privacy...
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...