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

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

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

DART: A Solution for decentralized federated learning model robustness analysis

Chao Feng, Alberto Huertas Celdrán, Jan von der Assen, Enrique Tomás Martínez Beltrán, Gérôme Bovet, Burkhard Stiller

Federated Learning (FL) has emerged as a promising approach to address privacy concerns inherent in Machine Learning (ML) practices. However, conventional FL methods, particularly those following the Centralized FL (CFL) paradigm, utilize a...