  Nowadays, machine learning algorithms continue to grow in complexity and
require a substantial amount of computational resources and energy. For these
reasons, there is a growing awareness of the development of new green
algorithms and distributed AI can contribute to this. Federated learning (FL)
is one of the most active research lines in machine learning, as it allows the
training of collaborative models in a distributed way, an interesting option in
many real-world environments, such as the Internet of Things, allowing the use
of these models in edge computing devices. In this work, we present a FL
method, 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. This allows obtaining an
effective and efficient model that simplifies the management of the training
process. Moreover, this method preserve data privacy by design, a crucial
aspect in current data protection regulations. We conducted experiments with
large datasets and a large number of federated clients. Despite being based on
a network model without hidden layers, it maintains in all cases competitive
accuracy results compared to more complex state-of-the-art machine learning
models. Furthermore, we show that the method performs equally well in both
identically and non-identically distributed scenarios. Finally, it is an
environmentally friendly algorithm as it allows significant energy savings
during the training process compared to its centralized counterpart.
