NEBULA: A Platform for Decentralized Federated Learning

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

2023 IEEE Communications Surveys & Tutorials

DOI Preprint

Fedstellar: A Platform for Decentralized Federated Learning

Enrique Tomás Martínez Beltrán, Ángel Luis Perales Gómez, Chao Feng, Pedro Miguel Sánchez Sánchez, Sergio López Bernal, Gérôme Bovet, Manuel Gil Pérez, Gregorio Martínez Pérez, Alberto Huertas Celdrán

2023 Expert Systems with Applications

DOI Preprint

Mitigating Communications Threats in Decentralized Federated Learning through Moving Target Defense

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

2024 Wireless Networks

DOI Preprint

Analyzing the robustness of decentralized horizontal and vertical federated learning architectures in a non-IID scenario

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

2024 Applied Intelligence

DOI

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

2024 Array

DOI

Sentinel: An Aggregation Function to Secure Decentralized Federated Learning

Chao Feng, Alberto Huertas Celdran, Janosch Baltensperger, Enrique Tomas Martinez Beltran, Gerome Bovet, Burkhard Stiller

2024 ECAI 2024

DOI

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

2024 Preprint

Preprint

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

2024 Preprint

Preprint

Privacy-preserving hierarchical federated learning with biosignals to detect drowsiness while driving

Sergio López Bernal, José Manuel Hidalgo Rogel, Enrique Tomás Martínez Beltrán, Mario Quiles Pérez, Gregorio Martínez Pérez, Alberto Huertas Celdrán

2024 Neural Computing and Applications

DOI

Reputation System based on Distributed Ledge to Secure Decentralized Federated Learning

Jan von der Assen, Sandrin Raphael Hunkeler, Alberto Huertas Celdran, Enrique Tomas Martinez Beltran, Gérôme Bovet, Burkhard Stiller

2024 Research Square

Preprint

TemporalFED: Detecting Cyberattacks in Industrial Time-Series Data Using Decentralized Federated Learning

Ángel Luis Perales Gómez, Enrique Tomás Martínez Beltrán, Pedro Miguel Sánchez Sánchez, Alberto Huertas Celdrán

2023 Preprint

Preprint

NEBULA

Publications

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Research Papers

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

Fedstellar: A Platform for Decentralized Federated Learning

Mitigating Communications Threats in Decentralized Federated Learning through Moving Target Defense

Analyzing the robustness of decentralized horizontal and vertical federated learning architectures in a non-IID scenario

Dart: A solution for decentralized federated learning model robustness analysis

Sentinel: An Aggregation Function to Secure Decentralized Federated Learning

Profe: Communication-efficient decentralized federated learning via distillation and prototypes

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

Privacy-preserving hierarchical federated learning with biosignals to detect drowsiness while driving

Reputation System based on Distributed Ledge to Secure Decentralized Federated Learning

TemporalFED: Detecting Cyberattacks in Industrial Time-Series Data Using Decentralized Federated Learning