This National Science Foundation (NSF) Project Grant award under the Computer and Information Science and Engineering program (CFDA 47.070) provides $131,520 to William Marsh Rice University to advance decentralized learning methods. The research aims to address fundamental challenges in communication efficiency, data heterogeneity, and algorithmic complexity to enable next-generation performance in decentralized learning systems. The work is structured around three thrusts: designing finite-time aggregation networks for faster convergence in decentralized optimization, developing algorithms to handle non-classical aggregated costs, and exploring high-order optimization methods to improve convergence rates and reduce communication costs. The project's contributions will advance the theoretical foundations of decentralized learning while offering practical solutions for scalable, efficient, distributed decision-making across fields such as machine learning, sensor networks, and autonomous systems. The award period runs from April 15, 2025, to March 31, 2030. No sub-awards are planned under this grant.
Mod # | Description | Reason For Modification | Federal Obligation (Click to sort descending) | Date (Click to sort ascending) |
|---|---|---|---|---|
| Not listed | $131.5k | 4/3/25 |