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 key products/services include: Developing computational theories, models, and prototype systems to establish the foundations for trustworthy DFL, balancing high-performance accuracy and security with privacy preservation. This includes investigating efficient stochastic bilevel optimization algorithms for emerging machine learning models in DFL settings. Thoroughly investigating unique security threats to DFL and developing principled defense strategies to provide security guarantees. Applying the developed techniques to practical data mining applications in Internet-of-Things networks and smart transportation to address unique challenges and provide real-world solutions. The project will also integrate the research into educational courses and provide research activities for undergraduate and graduate students.
Mod # | Description | Reason For Modification | Federal Obligation (Click to sort descending) | Date (Click to sort ascending) |
|---|---|---|---|---|
| Not listed | $380.7k | 8/12/24 |