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 National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Program (CFDA 47.070) project grant, awarded to North Carolina State University, is focused on enhancing privacy in federated learning, which is an AI approach enabling knowledge sharing without compromising data privacy. The $220,258 grant, awarded on October 1, 2024, aims to address vulnerabilities in federated learning schemes that may leak sensitive information through improper privacy...
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 $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...
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 $196,548 federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will support Temple University's research to develop a new Decentralized Federated Compositional Learning (DFCL) framework. The objective is to enable federated learning, an emerging collaborative machine learning paradigm, for the compositional learning paradigm. The project aims to address key computational and communication...
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's Division of Information and Intelligent Systems, under CFDA Program 47.070 (Computer and Information Science and Engineering), provides $130,000 to the University of Delaware to conduct collaborative research on critical learning periods in federated learning systems. The goal is to investigate and understand these critical periods during the federated learning training process, in order to enhance the security and robustness of...
Lehigh University received a $175,000 Project Grant award from the National Science Foundation under the Computer and Information Science and Engineering federal grant program (CFDA 47.070) to conduct research towards improving the handling of heterogeneity and personalization in federated learning. The University will develop mathematical models and efficient algorithms to address issues in heterogeneous federated learning caused by data and device diversity. Researchers will design advanced...