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UAV-asisted IoT network framework with hybrid deep reinforcement and federated learning
Andreas Andreou1, Constandinos X Mavromoustakis2, Evangelos Markakis3
1Department of Computer Science, University of Nicosia, 46 Makedonitissas Avenue, 1700, Nicosia, Cyprus. andreou.andreas@unic.ac.cy.
Scientific Reports
|October 23, 2025
Summary
This study integrates Unmanned Aerial Vehicle (UAV) deployment with privacy-preserving Federated Learning (FL) for IoT networks. The novel framework enhances data throughput and reduces latency, improving overall network efficiency.
Area of Science:
- Computer Science
- Electrical Engineering
- Network Engineering
Background:
- Unmanned Aerial Vehicle (UAV)-assisted Internet of Things (IoT) networks face challenges in optimizing UAV deployment, integrating Federated Learning (FL), and managing resources dynamically.
- Existing research often separates UAV placement optimization from FL-driven resource allocation, limiting network performance.
Purpose of the Study:
- To propose a unified hybrid framework for multi-UAV networks in IoT environments that integrates UAV deployment optimization, privacy-preserving FL, and adaptive resource allocation.
- To enhance cellular coverage, facilitate efficient data collection, and address dynamic network conditions.
Main Methods:
- Developed a hybrid framework leveraging Deep Reinforcement Learning (DRL) and FL.
- Introduced the Multi-UAV Network Formation (MUNF) algorithm using Particle Swarm Optimization (PSO) to enhance Signal-to-Noise Ratio (SNR).
- Implemented the Dynamic Adaptive Strategy (DAS) with Deep Deterministic Policy Gradient (DDPG) for resource allocation optimization.
Main Results:
- Achieved a 26% increase in data throughput.
- Demonstrated an 18% reduction in network latency.
- Showcased more stable SNR distribution compared to existing methods.
Conclusions:
- The proposed framework effectively addresses critical gaps in UAV-assisted IoT networks by unifying deployment, FL, and resource allocation.
- The results validate significant improvements in network efficiency, scalability, and performance under dynamic conditions.
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