Aprendizaje federado descentralizado y conciencia situacional para el reconocimiento aéreo militar seguro y resiliente

NEBULA - Decentralized Federated Learning for Heterogeneous Networks

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

Federated learning (FL) enables collaborative model training without sharing raw data, which is pivotal for maintaining privacy. However, existing FL frameworks often rely on a central coordinator, posing risks in heterogeneous networks. Th...

ProFe: Communication-Efficient Decentralized Federated Learning via Distillation and Prototypes

Pedro Miguel Sánchez Sánchez, Enrique Tomás Martínez Beltrán, Miguel Fernández Llamas, Gérôme Bovet, Gregorio Martínez Pérez, Alberto Huertas Celdrán

S-VOTE: Similarity-based Voting for Client Selection in Decentralized Federated Learning

Pedro Miguel Sánchez Sánchez, Enrique Tomás Martínez Beltrán, Chao Feng, Gérôme Bovet, Gregorio Martínez Pérez, Alberto Huertas Celdrán

Decentralized Federated Learning (DFL) enables collaborative, privacy-preserving model training without relying on a central server. This decentralized approach reduces bottlenecks and eliminates single points of failure, enhancing scalabil...