Federated Learning for Trust Enhancement in UAV-Enabled IoT Networks: A Unified Approach

Author First name, Last name, Institution

Ikram Ud Din, The University of Haripur
Imran Taj, Zayed University
Ahmad Almogren, College of Sciences
Mohsen Guizani, Mohamed Bin Zayed University of Artificial Intelligence

Document Type

Article

Source of Publication

IEEE Internet of Things Journal

Publication Date

1-1-2025

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 framework’s effectiveness in UAV networks and highlight its broader potential for IoT applications. Future work will explore further enhancements and diverse applications.

DOI Link

10.1109/jiot.2025.3542268

ISSN

2327-4662

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Disciplines

Computer Sciences

Keywords

Distributed Machine Learning, Federated Learning, IoT Security, Secure Data Aggregation, UAV Networks

Scopus ID

85217917285

Recommended Citation

Din, Ikram Ud; Taj, Imran; Almogren, Ahmad; and Guizani, Mohsen, "Federated Learning for Trust Enhancement in UAV-Enabled IoT Networks: A Unified Approach" (2025). All Works. 7115.
https://zuscholars.zu.ac.ae/works/7115

Indexed in Scopus

yes

Open Access

no