Project Grant N629092512078

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 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 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 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $145,830 to Old Dominion University Research Foundation (Odurf) to develop a secure and decentralized AI learning system for heterogeneous IoT networks. The project aims to design a novel distributed learning method that can effectively utilize diverse data types from various IoT devices without relying on a central server. It will also...

This three-year, $425,000 Project Grant from the National Science Foundation's Division of Electrical, Communications and Cyber Systems, under the Engineering program (CFDA 47.041), will fund research at The Pennsylvania State University to develop provably secure, online learning-based adaptive cyber defenses for real-world servers. The research aims to provide mathematically rigorous guarantees by synthesizing new models integrating machine learning, game theory, and control theory. It will...

This $348,573 project grant from the National Science Foundation's Division of Electrical, Communications and Cyber Systems, under the Engineering federal grant program (CFDA 47.041), will fund research at North Carolina State University from September 2022 through August 2025. The university will advance the frontiers of federated learning through exploring tradeoffs among learning performance, communication efficiency, privacy protection, and system robustness under a generalized...

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 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports the development of a learner-centered artificial intelligence (AI) co-pilot tool to crowd-source the curation of comprehensive cybersecurity attack artifacts. The $299,999 award to the Rochester Institute of Technology (RIT) aims to leverage large language models and techniques like prompt engineering to guide users in emulating end-to-end...

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 from the National Science Foundation's Computer and Information Science and Engineering program, totaling $247,903, supports research from November 2021 through September 2024 to develop techniques for improving the adaptability of machine learning-based security defenses. The goal is to enable these defenses to better handle dynamic changes in data caused by evolving attacks and changes in benign system usage, with reduced need for costly manual data labeling. The awardee,...