Zobrazeno 1 - 10
of 6 213
pro vyhledávání: '"Celdrán, A."'
Autor:
Mercedes Bermejo Boixareu
Publikováno v:
Clínica Contemporánea, Vol 14, Iss 3 (2023)
Externí odkaz:
https://doaj.org/article/44f6635a5d5d4b4692085e8117ea2803
Autor:
Feng, Chao, Guan, Hongjie, Celdrán, Alberto Huertas, von der Assen, Jan, Bovet, Gérôme, Stiller, Burkhard
Federated Learning (FL) performance is highly influenced by data distribution across clients, and non-Independent and Identically Distributed (non-IID) leads to a slower convergence of the global model and a decrease in model effectiveness. The exist
Externí odkaz:
http://arxiv.org/abs/2410.07678
Autor:
Celdrán, Alberto Huertas, Feng, Chao, Banik, Sabyasachi, Bovet, Gerome, Perez, Gregorio Martinez, Stiller, Burkhard
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 share the same set of features but different data samples, has been ex
Externí odkaz:
http://arxiv.org/abs/2410.06127
Autor:
Feng, Chao, Celdrán, Alberto Huertas, Zeng, Zien, Ye, Zi, von der Assen, Jan, Bovet, Gerome, Stiller, Burkhard
Decentralized Federated Learning (DFL), a paradigm for managing big data in a privacy-preserved manner, is still vulnerable to poisoning attacks where malicious clients tamper with data or models. Current defense methods often assume Independently an
Externí odkaz:
http://arxiv.org/abs/2409.19302
Autor:
Feng, Chao, Celdrán, Alberto Huertas, von der Assen, Jan, Beltrán, Enrique Tomás Martínez, Bovet, Gérôme, Stiller, Burkhard
Federated Learning (FL) has emerged as a promising approach to address privacy concerns inherent in Machine Learning (ML) practices. However, conventional FL methods, particularly those following the Centralized FL (CFL) paradigm, utilize a central s
Externí odkaz:
http://arxiv.org/abs/2407.08652
Autor:
Dan Deac
Publikováno v:
Archivo Español de Arqueología, Vol 94 (2021)
Externí odkaz:
https://doaj.org/article/0e7e3b24738443098d35f067301ff2f8
Autor:
Sánchez, Pedro Miguel Sánchez, Celdrán, Alberto Huertas, Bovet, Gérôme, Pérez, Gregorio Martínez
In the current cybersecurity landscape, protecting military devices such as communication and battlefield management systems against sophisticated cyber attacks is crucial. Malware exploits vulnerabilities through stealth methods, often evading tradi
Externí odkaz:
http://arxiv.org/abs/2405.09318
Autor:
von der Assen, Jan, Feng, Chao, Celdrán, Alberto Huertas, Oleš, Róbert, Bovet, Gérôme, Stiller, Burkhard
Although ransomware has received broad attention in media and research, this evolving threat vector still poses a systematic threat. Related literature has explored their detection using various approaches leveraging Machine and Deep Learning. While
Externí odkaz:
http://arxiv.org/abs/2401.17917
Autor:
Bernal, Eduardo López, Bernal, Sergio López, Pérez, Gregorio Martínez, Celdrán, Alberto Huertas
Brain-Computer Interfaces (BCIs) are used in various application scenarios allowing direct communication between the brain and computers. Specifically, electroencephalography (EEG) is one of the most common techniques for obtaining evoked potentials
Externí odkaz:
http://arxiv.org/abs/2311.05270
Autor:
Celdran, Alberto Huertas, Feng, Chao, Sanchez, Pedro Miguel Sanchez, Zumtaugwald, Lynn, Bovet, Gerome, Stiller, Burkhard
Artificial intelligence (AI) plays a pivotal role in various sectors, influencing critical decision-making processes in our daily lives. Within the AI landscape, novel AI paradigms, such as Federated Learning (FL), focus on preserving data privacy wh
Externí odkaz:
http://arxiv.org/abs/2310.20435