Collaborative Research: SaTC: CORE: Small: SHIELD: Enabling Multi-modal Distributed Learni — NSF Award to University of Alabama in
Unmanned Aerial Vehicles (UAVs) are becoming increasingly vital for applications such as disaster response, environmental monitoring, infrastructure inspection, and cybersecurity. These airborne platforms can collect diverse types of data in real time, offering valuable input for training high-performance machine learn
| Award title | Collaborative Research: SaTC: CORE: Small: SHIELD: Enabling Multi-modal Distributed Learni |
|---|---|
| Award ID | 2513164 |
| Awardee | University of Alabama in Huntsville |
| City | HUNTSVILLE |
| State | AL |
| Amount obligated | $599,830 |
| Principal investigator | Dinh Nguyen |
| Program | Secure &Trustworthy Cyberspace |
| Start date | 10/01/2025 |
| Abstract | Unmanned Aerial Vehicles (UAVs) are becoming increasingly vital for applications such as disaster response, environmental monitoring, infrastructure inspection, and cybersecurity. These airborne platforms can collect diverse types of data in real time, offering valuable input for training high-performance machine learning models. However, conventional machine learning techniques often rely on centralized training paradigms that require transmitting all raw data from UAVs to centralized servers: |
| Source | NSF Awards |
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