Project Grant 2315614
- This Project Grant award from the National Science Foundation's Division of Information and Intelligent Systems, under CFDA Program 47.070 (Computer and Information Science and Engineering), provides $130,000 to the University of Delaware to conduct collaborative research on critical learning periods in federated learning systems. The goal is to investigate and understand these critical periods during the federated learning training process, in order to enhance the security and robustness of...
- 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...
- The National Science Foundation (NSF) awarded a $402,229 Computer and Information Science and Engineering (CISE) Program grant to The Research Foundation For The State University Of New York, doing business as Stony Brook University. This 1-year grant, effective November 1, 2023, supports research and development focused on improving the security of machine learning (ML) systems that leverage third-party, pre-trained models. The project aims to develop rigorous methods for detecting and...
- 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...
- The National Science Foundation awarded a $249,997 two-year Project Grant to Stony Brook University under the Computer and Information Science and Engineering federal grant program (CFDA 47.070) to support research optimizing large-scale heterogeneous machine learning platforms. The Research Foundation for the State University of New York, operating on behalf of Stony Brook University, will deliver this collaborative research project from January 2022 through December 2024. The Computer and...
- 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...
- 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 Project Grant award from the National Science Foundation (CFDA 47.084 - NSF Technology, Innovation, and Partnerships) supports the development of FLTEST, an interdisciplinary testbed that automates privacy and robustness evaluations in federated learning systems. The $115,643 award, effective from October 1, 2025 through September 30, 2028, is aimed at creating standardized assessment tools to improve the reliability, validation, and trust in privacy-preserving artificial intelligence...
- 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 $300,000 National Science Foundation project grant under the STEM Education program (CFDA 47.076) aims to develop personalized secure programming learning projects motivated by real-world security vulnerabilities. The Research Foundation of the City University of New York at Brooklyn College will work with faculty to map secure programming topics to categories of vulnerabilities, build easy-to-use learning projects from selected vulnerabilities, and develop a personalized project delivery...
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 robustness and security of federated learning. Key activities include developing datasets, models, algorithms, and system source code to enhance federated learning security, and widely disseminating the research findings through publications, course materials, and tutorials. The project also engages undergraduate students, particularly from underrepresented groups, in the proposed research activities.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $170.0k | 8/21/23 |