Ponencia en conferencia2025

ICC 2025 - IEEE International Conference on Communications

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

TrainingQuantization (signal)CostsFederated learningPrototypesCollaborationData modelsComplexity theoryOptimizationFacesCommunication OptimizationAprendizaje federadoKnowledge DistillationPrototype LearningQuantization

Resumen

Autores

Pedro Miguel Sánchez SánchezEnrique Tomás Martínez BeltránMiguel Fernández LlamasGérôme BovetGregorio Martínez PérezAlberto Huertas Celdrán

Palabras clave

TrainingQuantization (signal)CostsFederated learningPrototypesCollaborationData modelsComplexity theoryOptimizationFacesCommunication OptimizationAprendizaje federadoKnowledge DistillationPrototype LearningQuantization

Publicaciones relacionadas

Publicaciones relacionadas por tema, método o aplicación.

Artículo de revista2023

IEEE Communications Surveys & Tutorials

Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges

Enrique Tomás Martínez Beltrán, Mario Quiles Pérez, Pedro Miguel Sánchez Sánchez, Sergio López Bernal, Gérôme Bovet, Manuel Gil Pérez, Gregorio Martínez Pérez, Alberto Huertas Celdrán

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 most common approach in the literature,...

Artículo de revista2025

IEEE Communications Magazine

Flighter: Decentralized Federated Learning and Situational Awareness for Secure Military Aerial Reconnaissance

Enrique Tomás Martínez Beltrán, Pedro Miguel Sánchez Sánchez, Gérôme Bovet, Burkhard Stiller, Gregorio Martínez Pérez, Alberto Huertas Celdrán

Mosaic warfare is a military strategy where reconnaissance missions with aerial vehicles are critical for gathering enemy information and achieving battlefield dominance. Nowadays, machine learning (ML) techniques pla...

Investigación relacionada