Raghad Alabagi
Papers
1
Total Citations
4
H-Index
1
About
Raghad Alabagi is a researcher at the forefront of federated learning and edge intelligence, with a focus on enabling privacy-preserving machine learning in resource-constrained environments. Her most-cited work, "Exploiting Federated Learning Technique to Recognize Human Activities in Resource-Constrained Environment" (2022), has garnered 4 citations and addresses a critical challenge: deploying accurate human activity recognition (HAR) systems on devices with limited computational power and bandwidth. By leveraging federated learning, Alabagi demonstrates how distributed models can collaboratively learn from decentralized data without compromising user privacy—a breakthrough for wearable and IoT applications. Her contributions bridge the gap between theoretical machine learning and practical deployment, offering scalable solutions for smart healthcare, assisted living, and ubiquitous computing. Alabagi’s research is particularly notable for its emphasis on efficiency and real-world feasibility, making her a rising voice in the intersection of edge computing and human-centered AI. Her work not only advances technical frontiers but also underscores the importance of ethical, resource-aware design in modern AI systems.
Research Focus
Key Achievements
Top Papers
- 1