An effective and efficient green federated learning method for one-layer neural networks
Óscar Fontenla-Romero, Bertha Guijarro‐Berdiñas, Elena Hernández-Pereira, Beatriz Pérez-Sánchez · 2024
We present a green Federated learning method (FL), based on a neural network without hidden layers, capable of generating a global collaborative model in a single training round, unlike traditional FL methods that require multiple rounds for convergence. Moreover, the method preserves data privacy by design, a crucial aspect of current data protection regulations. Experiments with large data sets and a large number of federated clients show that the model achieves competitive accuracy results compared to state-of-the-art machine learning models. Moreover, it performs equally well in identically and non-identically distributed scenarios.