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...
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...
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 University of Southern California was awarded a $499,548 Project Grant from the National Science Foundation Division of Computer and Network Systems under the Computer and Information Science and Engineering federal grant program (CFDA 47.070). The grant will support the university's work to accelerate privacy preserving deep learning techniques for real-time secure applications from July 1, 2021 to June 30, 2024. The Computer and Information Science and Engineering program aims to advance...
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 three-year $599,999 Project Grant from the National Science Foundation's Division of Computing and Communication Foundations, under the Computer and Information Science and Engineering program (CFDA 47.070), will support research into novel methods for computing aggregate statistics on streaming data in a privacy-preserving manner. Specifically, the University of California, Los Angeles will explore efficient algorithms to privately compute telemetry data from user devices sending...
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 $200,000 Project Grant award, provided by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070), supports the development of a novel multi-camera surveillance system for smart city applications. The project aims to create a scalable, efficient, and privacy-preserving real-time system that leverages distributed edge devices and cloud computing to capture and analyze video data. Key technical innovations include advanced...
This National Science Foundation (NSF) Division of Computing and Communication Foundations award, under CFDA Program 47.070, provides $146,219.00 in funding for a 4-year collaborative research project on the "Fundamental Limits of Cache-Aided Multi-User Private Function Retrieval." The project, conducted by the University of Utah, aims to develop a framework for privately retrieving the output of complex data processing functions from distributed, cache-aided computing nodes while...
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...