Enrique Tomás Martínez Beltrán

Asynchronous Cache-based Aggregation with Fairness and Filtering for Decentralized Federated Learning

Enrique Tomás Martínez Beltrán, Eduard Gash, Gérôme Bovet, Alberto Huertas Celdrán, Burkhard Stiller

Decentralized Federated Learning (DFL) offers a scalable paradigm for collaborative intelligence at the edge, yet its practical efficacy is severely constrained by system heterogeneity. Traditional synchronous protocols...

Decentralized Federated Learning with Multimodal Prototypes for Heterogeneous Data

Enrique Tomás Martínez Beltrán, Gérôme Bovet, Gregorio Martínez Pérez, Alberto Huertas Celdrán

Decentralized Federated Learning (DFL) enables collaborative machine learning across numerous devices while avoiding bottlenecks and reliance on a single trusted entity inherent to centralized architectures. However, its...

Decentralized Self-Supervised Representation Learning via Prototype Exchange under Non-IID Data

Enrique Tomás Martínez Beltrán, Gérôme Bovet, Gregorio Martínez Pérez, Alberto Huertas Celdrán

FedEnD: Communication-efficient Federated Learning for non-IID data via decentralized ensemble distillation

Enrique Tomás Martínez Beltrán, Philip Giryes, Gérôme Bovet, Burkhard Stiller, Gregorio Martínez Pérez, Alberto Huertas Celdrán

Federated Learning (FL) offers a paradigm for collaborative AI that mitigates raw data exposure, yet the statistical heterogeneity of client data severely constrains its practical application. This non-independent and id...

MadHoney: Señuelos Tóxicos para la Defensa Activa en el Aprendizaje Federado Descentralizado

Pedro Beltrán López, Enrique Tomás Martínez Beltrán, Pantaleone Nespoli, Manuel Gil Pérez, Gregorio Martínez Pérez, Alberto Huertas Celdrán