In recent years, Federated Learning (FL) has gained relevance in training collaborative
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …

Federated learning (FL) offers an effective learning architecture to protect data privacy in a
distributed manner. However, the inevitable network asynchrony, overdependence on a …

The growing concern over malicious attacks targeting the robustness of both centralized and
decentralized federated learning (FL) necessitates novel defensive strategies. In contrast to …

This paper explores the integration of Artificial Intelligence (AI) in access control to enhance
enterprise security, focusing on the synergy between human administrators and intelligent …

This is a B-mode project carried out under an employment contract with the company
Capgemini. In the recent years, it has been observed that clients are increasingly drawn to …

El Aprendizaje Federado Descentralizado emerge como una solución prometedora para
entrenar modelos de inteligencia artificial de manera colaborativa, sin compartir …

Abstract Decentralized Federated Learning (DFL) allows collaborative model training while
preserving data privacy, but it is vulnerable to poisoning attacks. This thesis proposes …