Project Grant 2403249
- 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...
- 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 (CFDA 47.084 - NSF Technology, Innovation, and Partnerships) supports the development of FLTEST, an interdisciplinary testbed that automates privacy and robustness evaluations in federated learning systems. The $115,643 award, effective from October 1, 2025 through September 30, 2028, is aimed at creating standardized assessment tools to improve the reliability, validation, and trust in privacy-preserving artificial intelligence...
- This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $274,636 to Rensselaer Polytechnic Institute (RPI) to conduct research on developing energy-efficient and scalable artificial intelligence (AI) systems. The key objectives are to: 1) leverage dynamic connectivity in AI models to reduce redundancy and energy consumption, 2) explore heterogeneous architectures integrating approximate,...
- This $381,264 project grant from the National Science Foundation's Computer and Information Science and Engineering program (CFDA 47.070) supports research at the University of Florida to develop hardware-efficient artificial intelligence techniques for federated learning across diverse Internet of Things devices. Over a three-year period ending September 2025, the research team will work to enable quantization and pruning of neural networks in a way that accounts for the varied computational...
- 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...
- This Project Grant award from the National Science Foundation (NSF) under the NSF Technology, Innovation, and Partnerships (CFDA 47.084) program provides $119,876 to the Regents of the University of Minnesota to develop FLTEST, an interdisciplinary testbed that automates privacy and robustness evaluations in federated learning systems. The research activities include developing automated test orchestration frameworks, implementing privacy attack simulation models, creating configuration...
- This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Project Grant award, with a total funding of $174,770, supports the development of an adaptive, federated, continuous learning system that uses a novel federated, semi-supervised learning framework. This framework aims to retrain deep neural network models on distributed, unlabeled, heterogeneous data from edge devices, while leveraging explainable AI techniques to expedite local training. The...
- This National Science Foundation (NSF) Project Grant award under the Computer and Information Science and Engineering (CFDA 47.070) program aims to develop software frameworks that can efficiently serve and deploy machine learning models for a variety of AI-powered applications. The $600,000 award, spanning from October 2024 to September 2027, tasks the prime awardee, Georgia Tech Research Corporation, with creating agile mechanisms and policies to serve a family of AI models across...
- This National Science Foundation (NSF) CISE Federal Grant Award (CFDA 47.070) in the amount of $200,000 will fund a collaborative research project between U.S. and Indian researchers to develop energy-efficient computation and communication methods for large language models (LLMs). The project aims to enhance the efficiency of training and inference for LLMs, which are crucial for advanced AI applications but are energy-intensive. By leveraging emerging high-speed networks and Compute Express...
This Project Grant award, valued at $214,970 and provided by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070), supports research to develop an energy-efficient framework for federated learning (FL) over mobile AI systems. The research aims to: 1) create a universal energy estimation methodology for FL training across diverse mobile devices; 2) explore strategies to enhance the energy efficiency of FL, particularly for high-speed communication scenarios; and 3) integrate learning performance metrics with energy parameters to optimize the FL participant selection process. This project establishes a mobile AI testbed and energy measurement setup to support these goals. The research outcomes seek to advance mobile computing and AI technologies, ensuring both are energy-efficient and privacy-preserving. The award is planned for the period of October 1, 2024 to September 30, 2028.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $215.0k | 7/15/24 |