CAREER: Ubiquitous and Time-Critical Federated Learning with Cooperative Mobile Edge Netwo — NSF Award to Texas A&M University (TX
Federated learning (FL) enables Internet-of-Things (IoT) devices at the network edge to collaboratively learn a shared prediction model while keeping all personal data on the device. However, the current cloud-based FL fails to meet the latency requirements of delay-sensitive IoT applications due to the long-distance t
| Award title | CAREER: Ubiquitous and Time-Critical Federated Learning with Cooperative Mobile Edge Netwo |
|---|---|
| Award ID | 2611068 |
| Awardee | Texas A&M University |
| City | COLLEGE STATION |
| State | TX |
| Amount obligated | $514,038 |
| Principal investigator | Yanmin Gong |
| Program | Networking Technology and Syst |
| Start date | 10/01/2025 |
| Abstract | Federated learning (FL) enables Internet-of-Things (IoT) devices at the network edge to collaboratively learn a shared prediction model while keeping all personal data on the device. However, the current cloud-based FL fails to meet the latency requirements of delay-sensitive IoT applications due to the long-distance transmission between IoT devices and the cloud. This project aims to enable ubiquitous and time-critical FL at the wireless edge to support delay-sensitive and data-driven IoT appli |
| Source | NSF Awards |
$799/mo
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