32 citations · 34 across the 5 of their papers we have counts for
5 papers
FedHENet: A Frugal Federated Learning Framework for Heterogeneous Environments
Alejandro Dopico-Castro, Oscar Fontenla-Romero, Bertha Guijarro-Berdiñas +2
Federated Learning (FL) enables collaborative training without centralizing data, essential for privacy compliance in real-world scenarios involving sensitive visual information. M…
CIFNet: An Analytic Neural Learning Framework for Efficient and Calibrated Class-Incremental Learning
Alejandro Dopico-Castro, Oscar Fontenla-Romero, Bertha Guijarro-Berdiñas +1
Class-Incremental Learning (CIL) in deep neural networks is conventionally framed as an iterative gradient-based optimization problem, incurring high computational cost, hyperparam…
An effective and efficient green federated learning method for one-layer neural networks
Oscar Fontenla-Romero, Bertha Guijarro-Berdiñas, Elena Hernández-Pereira +1
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 a…
Explained anomaly detection in text reviews: Can subjective scenarios be correctly evaluated?
David Novoa-Paradela, Oscar Fontenla-Romero, Bertha Guijarro-Berdiñas
This paper presents a pipeline to detect and explain anomalous reviews in online platforms. The pipeline is made up of three modules and allows the detection of reviews that do not…
Fast Deep Autoencoder for Federated learning
David Novoa-Paradela, Oscar Romero-Fontenla, Bertha Guijarro-Berdiñas
This paper presents a novel, fast and privacy preserving implementation of deep autoencoders. DAEF (Deep Autoencoder for Federated learning), unlike traditional neural networks, tr…