Project Grant 2241585
Award Date 8/15/23
Completion Date 7/31/26
Dollars Obligated $300K
Federal Agency
Federal Grant Program
Assistance Type
Project Grant
Place of Performance
Stillwater, OK 74078, USA
Similar Awards
- 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...
- This Project Grant award, titled "CAREER: ENABLING NEXT-GENERATION DECENTRALIZED LEARNING: STRUCTURE, MODELS, AND METHODS", was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070). The $131,520 grant, awarded on April 15, 2025, supports research to advance decentralized learning methods that improve data privacy, reduce communication bottlenecks, and enhance learning performance in...
- The National Science Foundation awarded a $1.2 million Project Grant to Stanford University under the Computer and Information Science and Engineering federal grant program (CFDA 47.070) for work on the Foundations of Decentralized Data Science from July 1, 2022 to June 30, 2025. The award will support development of schemes for performing common data science tasks like analytics and inference on distributed data located across networks without collecting all data in a single location. The...
- 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 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 $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 $400,000 federal Project Grant award was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070) to The Ohio State University. The grant supports collaborative research to develop a principled and unified mathematical framework for deep learning on low-dimensional data structures. The key objectives are to: 1) Design "white-box" deep neural networks optimized for information gain and representation...
- The National Science Foundation (NSF) awarded a $400,000 Project Grant under its Computer and Information Science and Engineering (CISE) program to Georgia Tech Research Corporation, doing business as the Office of Sponsored Programs. The grant supports a 4-year collaborative research project to develop innovative, privacy-preserving machine learning algorithms for analyzing graph-structured data. Key objectives include designing non-uniform privatization protocols to balance data utility and...
- The National Science Foundation (NSF) awarded a $400,000 Project Grant under the Computer and Information Science and Engineering (CFDA #47.070) program to the University of Illinois for a 4-year collaborative research project on privacy-preserving machine learning on graph-structured data. The project aims to develop innovative, efficient algorithms for training and updating large-scale graph neural network models while preserving the privacy of sensitive graph data across applications in areas...
- This Project Grant award from the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program provides $488,384 to Oklahoma State University (OSU) to develop decentralized, privacy-preserving methods for detecting cyber attacks on critical infrastructure networks. The key products and services to be delivered include: Decentralized algorithms and computational frameworks for publicly verifiable cyber attack detection, leveraging...