EnBaSe: Enhancing Image Classification in IoT Scenarios through Entropy-Based Selection of Non-IID Data

This study presents an analysis of the scalability and dispersion of results in Federated Learning (FL) using two algorithms: EnBaSe, based on entropy, and Random, a random selection approach. The Random algorithm ensures that each member of the population has an equal probability of inclusion. At the same time, EnBaSe calculates the information gain and selects the most informative samples for the neural network. Both algorithms were applied in federated learning scenarios with data distributed non-independently and non-identically (Non-IID). The MNIST, Fashion-MNIST, CIFAR-10, and CIFAR-100 datasets were used for the evaluation, representing different levels of computer vision classification. The results show that the EnBaSe algorithm achieves high accuracy while halving computational and energy costs compared to training with all samples from the datasets. In addition, EnBaSe demonstrated greater resilience to variability, showing low variance and a more stable distribution, especially in Internet of things (IoT) environments with limited computational resources.