BatFed: Battery-Aware Federated Transfer Learning Strategy for Remote Monitoring by UAV

• Hector Batista Ribeiro UFPI
• Anderson Thyago De Jesus Carvalho Baptista UFPI
• Mizael Pereira Gonçalves UFPI
• Ricardo De Andrade Lira Rabelo UFPI
• José Rodrigues Torres Neto UFPI

Resumo

Unmanned aerial vehicles (UAVs) have been widely used in various Internet of Things (IoT) applications. However, given the stringent limitations of UAVs, strategies to maximize computational efficiency considering these devices’ processing and energy constraints have recently attracted significant interest. This work presents BatFed, a battery-aware strategy for federated machine learning based on transfer learning for remote monitoring using UAVs. The MobileNetV3Small architecture was employed as the foundation for developing a model capable of maximizing computational efficiency on edge devices with processing and energy constraints, while the EuroSAT_RGB dataset served as the evaluation basis. Our strategy employs dynamic client selection to reduce the communication rate between devices and the central server, promoting greater bandwidth efficiency. Furthermore, the experiments, made in a non-IID scenario using Dirichlet distribution to simulate a moderately heterogeneous scenario (α = 1.0) achieved an accuracy exceeding 80% in most cases, reaching over 90% accuracy with additional local training epochs. Compared to our centralized learning baseline, which achieved 95% accuracy, this is a positive result with other significant advantages in terms of time savings, energy consumption and communication savings, demonstrating its feasibility for distributed monitoring applications.

Palavras-chave: Training, Accuracy, Spectral efficiency, Transfer learning, Autonomous aerial vehicles, Computational efficiency, Internet of Things, Servers, Vehicle dynamics, Remote monitoring, Federated Learning, Transfer Learning, Unmanned Aerial Vehicles, Remote Monitoring, Client Selection