Project Grant 2452818
- 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...
- 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 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 from the National Science Foundation (CFDA 47.084 - NSF Technology, Innovation, and Partnerships) will fund the planning of an AI-ready wireless network testbed to enable development and testing of AI-driven solutions for critical industries including agriculture, transportation, and smart cities. The $199,636 award to Virginia Polytechnic Institute & State University (Virginia Tech) will facilitate the design of a large-scale testbed infrastructure featuring an...
- 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 $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 $300,000 Project Grant from the National Science Foundation Division of Civil, Mechanical, and Manufacturing Innovation, under the Engineering federal grant program (CFDA 47.041), will support research at Virginia Polytechnic Institute and State University to develop a cost-sensitive federated artificial intelligence framework called COFEDAI. The goal is to facilitate data sharing and aggregation across multiple manufacturers to improve AI and machine learning for smart manufacturing...
- 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 $300,000 EAGER project grant, awarded by the National Science Foundation (NSF) under its Integrative Activities program (CFDA 47.083), aims to develop a framework for better safeguarding AI research from growing security threats. Led by Virginia Polytechnic Institute & State University (Virginia Tech), the project will explore how to map potential threats across the AI research lifecycle and help institutions, educators, and policymakers address vulnerabilities more effectively. Through...
- 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) 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 evaluation of privacy attack simulations, configuration vulnerability detection, and optimization recommendations to streamline the assessment of federated learning systems. This work is intended to enable secure collaboration on AI development while protecting individual privacy, supporting U.S. competitiveness in AI, and strengthening data security across critical infrastructure. The project will be conducted from October 1, 2025 through September 30, 2028.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $121.7k | 8/19/25 |