27 citations · 76 across the 20 of their papers we have counts for
21 papers
DecoyTrace: Toxic Decoys for Active Defense in Decentralized Federated Learning
Pedro Beltrán-López, Enrique Tomás Martínez Beltrán, Pantaleone Nespoli +2
Decentralized Federated Learning (DFL) eliminates the central aggregation server, reducing the single point of observation that traditional defenses against attacks rely on. As a r…
SYNAPSE: Framework for Neuron Analysis and Perturbation in Sequence Encoding
Jesús Sánchez Ochoa, Enrique Tomás Martínez Beltrán, Alberto Huertas Celdrán
In recent years, Artificial Intelligence has become a powerful partner for complex tasks such as data analysis, prediction, and problem-solving, yet its lack of transparency raises…
Neural cyberattacks applied to the vision under realistic visual stimuli
Victoria Magdalena López Madejska, Sergio López Bernal, Gregorio Martínez Pérez +1
Brain-Computer Interfaces (BCIs) are systems traditionally used in medicine and designed to interact with the brain to record or stimulate neurons. Despite their benefits, the lite…
DMPA: Model Poisoning Attacks on Decentralized Federated Learning for Model Differences
Chao Feng, Yunlong Li, Yuanzhe Gao +4
Federated learning (FL) has garnered significant attention as a prominent privacy-preserving Machine Learning (ML) paradigm. Decentralized FL (DFL) eschews traditional FL's central…
GreenDFL: a Framework for Assessing the Sustainability of Decentralized Federated Learning Systems
Chao Feng, Alberto Huertas Celdrán, Xi Cheng +2
Decentralized Federated Learning (DFL) is an emerging paradigm that enables collaborative model training without centralized data and model aggregation, enhancing privacy and resil…
S-VOTE: Similarity-based Voting for Client Selection in Decentralized Federated Learning
Pedro Miguel Sánchez Sánchez, Enrique Tomás Martínez Beltrán, Chao Feng +3
Decentralized Federated Learning (DFL) enables collaborative, privacy-preserving model training without relying on a central server. This decentralized approach reduces bottlenecks…
ProFe: Communication-Efficient Decentralized Federated Learning via Distillation and Prototypes
Pedro Miguel Sánchez Sánchez, Enrique Tomás Martínez Beltrán, Miguel Fernández Llamas +3
Decentralized Federated Learning (DFL) trains models in a collaborative and privacy-preserving manner while removing model centralization risks and improving communication bottlene…
FedEP: Tailoring Attention to Heterogeneous Data Distribution with Entropy Pooling for Decentralized Federated Learning
Chao Feng, Hongjie Guan, Alberto Huertas Celdrán +3
Non-Independent and Identically Distributed (non-IID) data in Federated Learning (FL) causes client drift issues, leading to slower convergence and reduced model performance. While…
De-VertiFL: A Solution for Decentralized Vertical Federated Learning
Alberto Huertas Celdrán, Chao Feng, Sabyasachi Banik +3
Federated Learning (FL), introduced in 2016, was designed to enhance data privacy in collaborative model training environments. Among the FL paradigm, horizontal FL, where clients…
DART: A Solution for Decentralized Federated Learning Model Robustness Analysis
Chao Feng, Alberto Huertas Celdrán, Jan von der Assen +3
Federated Learning (FL) has emerged as a promising approach to address privacy concerns inherent in Machine Learning (ML) practices. However, conventional FL methods, particularly…
Evaluation of Data Processing and Machine Learning Techniques in P300-based Authentication using Brain-Computer Interfaces
Eduardo López Bernal, Sergio López Bernal, Gregorio Martínez Pérez +1
Brain-Computer Interfaces (BCIs) are used in various application scenarios allowing direct communication between the brain and computers. Specifically, electroencephalography (EEG)…
FederatedTrust: A Solution for Trustworthy Federated Learning
Pedro Miguel Sánchez Sánchez, Alberto Huertas Celdrán, Ning Xie +3
The rapid expansion of the Internet of Things (IoT) and Edge Computing has presented challenges for centralized Machine and Deep Learning (ML/DL) methods due to the presence of dis…
Adversarial attacks and defenses on ML- and hardware-based IoT device fingerprinting and identification
Pedro Miguel Sánchez Sánchez, Alberto Huertas Celdrán, Gérôme Bovet +1
In the last years, the number of IoT devices deployed has suffered an undoubted explosion, reaching the scale of billions. However, some new cybersecurity issues have appeared toge…
When Brain-Computer Interfaces Meet the Metaverse: Landscape, Demonstrator, Trends, Challenges, and Concerns
Sergio López Bernal, Mario Quiles Pérez, Enrique Tomás Martínez Beltrán +2
The metaverse has gained tremendous popularity in recent years, allowing the interconnection of users worldwide. However, current systems in metaverse scenarios, such as virtual re…
Trust-as-a-Service: A reputation-enabled trust framework for 5G networks
José María Jorquera Valero, Pedro Miguel Sánchez Sánchez, Manuel Gil Pérez +2
Trust, security, and privacy are three of the major pillars to assemble the fifth generation network and beyond. Despite such pillars are principally interconnected, they arise a m…
Data Fusion in Neuromarketing: Multimodal Analysis of Biosignals, Lifecycle Stages, Current Advances, Datasets, Trends, and Challenges
Mario Quiles Pérez, Enrique Tomás Martínez Beltrán, Sergio López Bernal +4
The primary goal of any company is to increase its profits by improving both the quality of its products and how they are advertised. In this context, neuromarketing seeks to enhan…
LwHBench: A low-level hardware component benchmark and dataset for Single Board Computers
Pedro Miguel Sánchez Sánchez, José María Jorquera Valero, Alberto Huertas Celdrán +3
In today's computing environment, where Artificial Intelligence (AI) and data processing are moving toward the Internet of Things (IoT) and Edge computing paradigms, benchmarking r…
Eight Reasons Why Cybersecurity on Novel Generations of Brain-Computer Interfaces Must Be Prioritized
Sergio López Bernal, Alberto Huertas Celdrán, Gregorio Martínez Pérez
This article presents eight neural cyberattacks affecting spontaneous neural activity, inspired by well-known cyberattacks from the computer science domain: Neural Flooding, Neural…
Neuronal Jamming Cyberattack over Invasive BCI Affecting the Resolution of Tasks Requiring Visual Capabilities
Sergio López Bernal, Alberto Huertas Celdrán, Gregorio Martínez Pérez
Invasive Brain-Computer Interfaces (BCI) are extensively used in medical application scenarios to record, stimulate, or inhibit neural activity with different purposes. An example…
Federated Learning for Malware Detection in IoT Devices
Valerian Rey, Pedro Miguel Sánchez Sánchez, Alberto Huertas Celdrán +2
This work investigates the possibilities enabled by federated learning concerning IoT malware detection and studies security issues inherent to this new learning paradigm. In this…
AuthCODE: A Privacy-preserving and Multi-device Continuous Authentication Architecture based on Machine and Deep Learning
Pedro Miguel Sánchez Sánchez, Alberto Huertas Celdrán, Lorenzo Fernández Maimó +1
The authentication field is evolving towards mechanisms able to keep users continuously authenticated without the necessity of remembering or possessing authentication credentials.…