Project Grant 2452817
- 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...
- 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 $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...
- 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 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 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 (NSF) Directorate for Engineering (CFDA 47.041) awarded a $472,000 Project Grant to the Regents of the University of Minnesota, a non-profit 1862 land grant college, to develop a general framework for designing and analyzing decentralized and federated learning systems. The proposed work aims to unify various distributed algorithms and provide insights to streamline their design and analysis across a range of applications beyond machine learning, such as control...
- The National Science Foundation (NSF) Engineering program (CFDA 47.041) awarded a $199,993 project grant to the University of North Dakota (UND) to develop a novel data synthesis framework for edge-based artificial intelligence (AI) applications in network measurement. The grant, awarded from October 1, 2025 to September 30, 2027, aims to address the challenge of providing accurate and up-to-date data to edge AI applications while preserving user privacy and minimizing communication overhead....
- 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 $500,000 Project Grant under the Engineering program (CFDA 47.041) to Wayne State University, located in Detroit, Michigan. The goal of this 5-year research project, which commenced on October 1, 2025, is to investigate fundamental limits and algorithmic principles for trustworthy sequential decision-making in artificial intelligence and machine learning (AI/ML) systems, particularly those powered by reinforcement learning. The research focuses...
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 reliability of these systems, enabling secure collaboration on AI development while protecting individual privacy. Key research activities include developing automated test orchestration frameworks, implementing privacy attack simulation models, creating configuration vulnerability detection systems, and building recommendation engines for optimization. This project supports American competitiveness in AI technologies and strengthens data security across critical infrastructure.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $119.9k | 8/19/25 |