10 papers
SYNAPSE: Framework for Neuron Analysis and Perturbation in Sequence Encoding
Jesús Sánchez Ochoa, Enrique Tomás MartÃnez 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 MartÃnez 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…
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…
When Brain-Computer Interfaces Meet the Metaverse: Landscape, Demonstrator, Trends, Challenges, and Concerns
Sergio López Bernal, Mario Quiles Pérez, Enrique Tomás MartÃnez 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…
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…
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…