3 papers
Closing the Alignment-Maturity Gap in Federated Prototype Learning
Mario Casado-Diez, Alejandro Dopico-Castro, Verónica Bolón-Canedo +1
Learning discriminative visual representations from distributed, heterogeneous data is a fundamental challenge in Federated Learning (FL). Prototype-based methods address statistic…
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…