581 citations · 584 across the 4 of their papers we have counts for
5 papers
Trustworthy, Explainable, and Sustainable Decentralized Intelligence for 6G Networks
Giovanni Perin, Michele Rossi, Enrique Tomás Martínez Beltrán +13
As 6G networks transition from theoretical frameworks into operational realities, artificial intelligence (AI) evolves from an add-on optimization tool into a distributed and inter…
RepuNet: A Reputation System for Mitigating Malicious Clients in DFL
Isaac Marroqui Penalva, Enrique Tomás Martínez Beltrán, Manuel Gil Pérez +1
Decentralized Federated Learning (DFL) enables nodes to collaboratively train models without a central server, introducing new vulnerabilities since each node independently selects…
Sentinel: An Aggregation Function to Secure Decentralized Federated Learning
Chao Feng, Alberto Huertas Celdrán, Janosch Baltensperger +4
Decentralized Federated Learning (DFL) emerges as an innovative paradigm to train collaborative models, addressing the single point of failure limitation. However, the security and…
TemporalFED: Detecting Cyberattacks in Industrial Time-Series Data Using Decentralized Federated Learning
Ángel Luis Perales Gómez, Enrique Tomás Martínez Beltrán, Pedro Miguel Sánchez Sánchez +1
Industry 4.0 has brought numerous advantages, such as increasing productivity through automation. However, it also presents major cybersecurity issues such as cyberattacks affectin…
Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges
Enrique Tomás Martínez Beltrán, Mario Quiles Pérez, Pedro Miguel Sánchez Sánchez +5
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 mo…