This Project Grant award for $249,999 from the National Science Foundation's Engineering program (CFDA 47.041) will support the development of a novel communication-efficient hierarchical distributed optimization framework that integrates optimization, communication, and machine learning. The key objectives are to: (1) develop a general framework for learning-enabled hierarchical distributed optimization algorithms; (2) create methods for learning-assisted adaptive quantization, communication, and query; (3) establish a unifying machine learning framework to balance communication savings and computational accuracy; and (4) integrate and validate the framework through real-life applications like sensor networks, cooperative robotics, federated learning, and smart grids. The award will be performed by the University of Central Florida from October 1, 2024 to March 31, 2027. The project aims to advance knowledge across optimization, communication, and machine learning, with broader impacts including supporting critical modern engineering infrastructure and providing educational opportunities for undergraduate students.
Mod # | Description | Reason For Modification | Federal Obligation (Click to sort descending) | Date (Click to sort ascending) |
|---|---|---|---|---|
| Not listed | $250.0k | 11/14/24 |