Project Grant 2440610
- 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 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 $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 $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 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 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...
- 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 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 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 $398,786 Project Grant award from the National Science Foundation (NSF) Engineering program (CFDA 47.041) supports research at North Carolina State University to develop a privacy-preserving collaborative condition monitoring and decision-making methodology for distributed manufacturing systems. The project aims to enable multiple geographically distributed manufacturing facilities to collectively utilize their data to construct more effective monitoring and decision-making models, while...
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 Federated Learning models in dynamic environments, while maintaining strong privacy protections. This seeks to overcome limitations of existing game theory and reinforcement learning approaches. The research will develop innovative methods to balance privacy and accuracy in Federated Learning, enabling its broader adoption in real-world applications such as traffic management and autonomous systems.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $514.0k | 8/18/25 |