Project Grant 2139304
- 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...
- 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...
- 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 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...
- 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 two-year, $299,998 Project Grant from the National Science Foundation's Computer and Information Science and Engineering program will fund research quantifying the fair value of data and privacy in distributed learning environments. The grantee, the Regents of the University of California at Berkeley, will develop a framework for systematically quantifying the value of data at various privacy levels using techniques from economics, game theory, optimization, machine learning and statistics....
- The University of California, San Diego (UCSD) was awarded a $400,000 Project Grant by the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Program. The 4-year grant, starting on May 1, 2024, will fund research to develop innovative privacy-preserving machine learning algorithms for graph-structured data, which has widespread applications in areas like communication theory, computational biology, and social sciences. The project aims to establish a...
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 devices. The Principal Investigator will explore privacy mechanisms robust to iterative interactions in collaborative learning and analyze associated privacy-performance tradeoffs. Additionally, the research leverages ideas from robust statistics and information theory to develop secure mechanisms enabling personalized learning despite malicious participants, investigating when collaboration provides benefits. Outcomes include advancing the state of art at the intersection of information theory, trusted federated machine learning, and optimization through formal privacy and security guarantees.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $500.0k | 2/22/22 |