Federated Learning for Trust Enhancement in UAV-Enabled IoT Networks: A Unified Approach
Ud Din, Ikram Taj, Imran Almogren, Ahmad Guizani, Mohsen
Ud Din, Ikram
Taj, Imran
Almogren, Ahmad
Guizani, Mohsen
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Department
Machine Learning
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Journal article
Date
2025
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English
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Abstract
This study presents a federated learning (FL) framework tailored for UAV-enabled IoT networks, addressing challenges in efficiency, robustness, and scalability. The proposed system improves model learning with a 14.9 percentage point increase in accuracy (75.5% to 90.4%) and a 69.2% reduction in loss over ten training epochs. It demonstrates resilience, limiting accuracy reduction to 7% under simulated attacks, and scalability with a linear increase in processing times as network size grows. High anomaly detection rates (92%) further enhance network security and reliability. These results validate the frameworkâs effectiveness in UAV networks and highlight its broader potential for IoT applications. Future work will explore further enhancements and diverse applications.
Citation
I. U. Din, I. Taj, A. Almogren and M. Guizani, "Federated Learning for Trust Enhancement in UAV-Enabled IoT Networks: A Unified Approach," in IEEE Internet of Things Journal, doi: 10.1109/JIOT.2025.3542268
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IEEE Internet of Things Journal
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Keywords
Federated Learning, UAV Networks, IoT Security, Secure Data Aggregation, Distributed Machine Learning
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IEEE