Proceedings of the ACM SIGCOMM 2025 Posters and Demos
NEBULA - Decentralized Federated Learning for Heterogeneous Networks
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 het...
Resumen
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. This work presents NEBULA, a decentralized FL platform that unifies centralized and peer-to-peer FL paradigms, integrating network awareness and autonomous adaptation for improved resilience and efficiency. Key contributions include: (1) a unified architecture supporting both server-coordinated and fully decentralized operation; (2) network-aware orchestration for dynamic communication and aggregation optimization; and (3) built-in mechanisms for robust operation. The demonstration will showcase real-time performance, defense against adversarial attacks, and adaptive client participation in challenging network scenarios.
Autores
Palabras clave
Publicaciones relacionadas
Publicaciones relacionadas por tema, método o aplicación.
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...
X Jornadas Nacionales de Investigación en Ciberseguridad (JNIC 2025)
Aprendizaje federado descentralizado y conciencia situacional para el reconocimiento aéreo militar seguro y resiliente
Enrique Tomás Martínez Beltrán, Miguel Fernández Llamas, Anas Zine Boujemaoui, Gérôme Bovet, Gregorio Martínez Pérez, Alberto Huertas Celdrán
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,...
Investigación relacionada
Dic 2022 / Nov 2025
EU-GUARDIAN: marco europeo y pruebas de concepto para la automatización inteligente de la gestión de incidentes de ciberdefensa
Proyecto europeo de investigación sobre métodos y pruebas de concepto para apoyar la gestión de incidentes de ciberdefensa.
Ene 2024 / Jun 2026
ROBUST-6G: plataforma inteligente, automatizada y fiable de servicios de seguridad para 6G
ROBUST-6G estudia mecanismos de seguridad para sistemas 6G, incluyendo monitorización, gestión segura de datos, servicios de IA confiable, aprendizaje federado y respuesta ante amenazas.
Ene 2025 / Dic 2025
DECIMAL: Aprendizaje adaptativo multimodal inteligente y descentralizado
Colaboración completada con el Cyber-Defence Campus de armasuisse que desarrolló aprendizaje robusto, eficiente y adaptativo para sensores multimodales.