Novel regularized training method for one-layer neural networks (no hidden layers) that uses an L2-norm penalty term. This noniterative supervised method uses a closed-form expression and determines the optimum set of weights by solving a system of linear equations. It also allows incremental and distributed privacy-preserving learning, which means a perfect fit in federated learning environments.
This method was published in the following article:
Fontenla-Romero O., Guijarro-Berdiñas B., Pérez-Sánchez B. (2021) Regularized One-Layer Neural Networks for Distributed and Incremental Environments. In: Rojas I., Joya G., Català A. (eds) Advances in Computational Intelligence. IWANN 2021. Lecture Notes in Computer Science, vol 12862. Springer, Cham. https://doi.org/10.1007/978-3-030-85099-9_28