UAV-Assisted Federated Learning with Autoencoders for IoT Image Classification
André Martins Gonçalves, Bruno José Olivieri de Souza, Markus Endler · 2025
The exponential growth of the Internet of Things (IoT) has introduced unprecedented challenges in data processing, privacy preservation, and energy efficiency. Traditional centralized approaches are often unsuitable for IoT environments due to bandwidth limitations, data heterogeneity, and privacy concerns. This study proposes a novel framework combining federated learning (FL) and autoencoders to address these issues in IoT-based image classification tasks. By lever-aging Unmanned Aerial Vehicles (UAVs) as intermediaries for model aggregation and distribution, the framework minimizes communication overhead while maintaining data privacy. Autoencoders are employed for unsupervised feature extraction, enabling effective data representation even in the absence of labeled data. Results demonstrate that, while autoencoders achieve lower classification accuracy compared to supervised approaches, they provide significant advantages in bandwidth efficiency, scalability, and privacy preservation. The integration of UAVs further enhances the system by optimizing communication and enabling model improvement in real-time. This framework offers a flexible and resource-efficient solution for IoT applications, particularly in scenarios where data labeling is impractical or privacy is paramount.