Project Grant FA23862314064

This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) provides $199,999 to San Francisco State University to develop a resilient next-generation (NextG) network design for federated learning over mobile devices. The key products and services to be delivered under this 2-year award include: Exploiting serverless computing at the network edge to efficiently provide machine learning computing...

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 $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 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...

The National Science Foundation awarded a $219,999 Project Grant under the Engineering program (CFDA 47.041) to the University of California, Davis for research titled "CCSS: COLLABORATIVE RESEARCH: QUALITY-AWARE DISTRIBUTED COMPUTATION FOR WIRELESS FEDERATED LEARNING: CHANNEL-AWARE USER SELECTION, MINI-BATCH SIZE ADAPTATION, AND SCHEDULING." The award period is from October 1, 2021 through July 31, 2024. The research will develop techniques for channel-aware user selection, mini-batch...

This $500,000 Project Grant from the National Science Foundation's Computer and Information Science and Engineering program will fund research into information-theoretic privacy and security for personalized distributed learning systems at the University of California, Los Angeles from March 2022 through February 2025. The grant aims to design personalized learning models that leverage large-scale collaborative data while maintaining individuals' privacy and requiring trust only in one's own...

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...

The National Science Foundation Division of Computing and Communication Foundations awarded a $614,000 Project Grant to the University of California, Los Angeles to support research into efficient compression schemes for communication-constrained machine learning environments. Under the Computer and Information Science and Engineering program (CFDA 47.070), this three-year award will fund the development of novel techniques to compress data communicated between distributed learning agents,...

This Project Grant award from the National Science Foundation's Computer and Information Science and Engineering (CISE) program, CFDA 47.070, provides $149,990 to the Stevens Institute of Technology to develop a novel next-generation (NextG) network architecture that can support resilient federated learning over mobile devices. The project aims to address challenges faced by resource-constrained stakeholders in intelligent mobile applications and services, such as spectrum, energy, and computing...