Project Grant 2452819
- The National Science Foundation (NSF) awarded a $119,876 Project Grant under the NSF Technology, Innovation, and Partnerships (CFDA 47.084) program to the Regents of the University of Minnesota to design, develop, and sustain FLTEST, an interdisciplinary testbed that automates privacy and robustness evaluations in federated learning systems. This project aims to address challenges in existing privacy-preserving AI systems by developing comprehensive testing tools that can verify the...
- This National Science Foundation (NSF) Project Grant award under the Technology, Innovation, and Partnerships (CFDA 47.084) program provides $121,650 to Virginia Polytechnic Institute & State University (Virginia Tech) to develop the FLTEST testbed. The project aims to address challenges in verifying the privacy and robustness of federated learning systems, which enable AI model training across multiple data sources without directly sharing private data. The testbed will automate the...
- 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 Project Grant award, valued at $214,970 and provided by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070), supports research to develop an energy-efficient framework for federated learning (FL) over mobile AI systems. The research aims to: 1) create a universal energy estimation methodology for FL training across diverse mobile devices; 2) explore strategies to enhance the energy efficiency of FL, particularly for...
- This $117,451 federal Project Grant awarded by the National Science Foundation (NSF) under the NSF Technology, Innovation, and Partnerships (TIP) program (CFDA 47.084) supports research to develop novel approaches that can help Public Interest Technology (PIT) organizations deploy data safeguards when building AI systems. The project engages a multi-disciplinary team to conduct ethnographic and computational research on using disclosure limitation techniques, including differential privacy, to...
- 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 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 Project Grant award from the National Science Foundation (CFDA 47.084 - NSF Technology, Innovation, and Partnerships) to Virginia Polytechnic Institute & State University supports the development of methods to allow organizations to safely share machine learning models trained on sensitive patient data without compromising individual privacy. The $128,985 project, running from October 2025 to September 2028, aims to create new techniques for auditing models, certifying their privacy...
- This Project Grant award, valued at $150,000.00, was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) Federal Grant Program. The project aims to develop a novel Next Generation (NextG) network design to support resilient Federated Learning (FL) over large-scale heterogeneous mobile devices. Key technical objectives include: Exploiting serverless computing at the network edge to provide resilient and efficient ML...
- This $513,998 Project Grant awarded by the National Science Foundation's Engineering program (CFDA 47.041) aims to address key challenges in Federated Learning, a privacy-preserving collaborative machine learning approach. The grant was awarded to North Carolina Agricultural and Technical State University (NC A&T) on October 1, 2025, with a final completion date of September 30, 2030. The project will focus on designing strategies to fairly incentivize clients contributing data to...
This Project Grant award from the National Science Foundation (CFDA 47.084 - NSF Technology, Innovation, and Partnerships) provides $115,643.00 to develop FLTEST, an interdisciplinary testbed to automate privacy and robustness evaluations in federated learning systems. The project aims to address gaps in existing tools by developing automated test orchestration frameworks, implementing privacy attack simulation models, creating configuration vulnerability detection systems, and building recommendation engines for optimization. This research supports secure collaboration on AI development while protecting individual privacy, enabling American competitiveness in AI technologies, and strengthening data security across critical infrastructure. The University of Massachusetts in Amherst is the primary awardee, conducting this work from October 1, 2025 through September 30, 2028.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $115.6k | 8/19/25 |