Zobrazeno 1 - 10
of 4 804
pro vyhledávání: '"Pedro A, Sánchez"'
Publikováno v:
Tiempos Modernos; jun2022, Vol. 12 Issue 44, p241-263, 23p
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
Publikováno v:
Revista de Ciencias Médicas de Pinar del Río, Vol 20, Iss 3, Pp 350-358 (2016)
Introducción: la evolución de las áreas de salud adquiere connotación especial, permitiendo revelar tendencias y regularidades de las instituciones donde se prestan servicios médicos, así como desentrañar sus orígenes y resultados. Objetivo:
Externí odkaz:
https://doaj.org/article/8f278f1703254abbb33cf6fe81d1f51d
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
Autor:
Feng, Chao, Celdrán, Alberto Huertas, Baltensperger, Janosch, Beltrán, Enrique Tomás Martínez, Sánchez, Pedro Miguel Sánchez, Bovet, Gérôme, Stiller, Burkhard
Decentralized Federated Learning (DFL) emerges as an innovative paradigm to train collaborative models, addressing the single point of failure limitation. However, the security and trustworthiness of FL and DFL are compromised by poisoning attacks, n
Externí odkaz:
http://arxiv.org/abs/2310.08097
Autor:
Julia K. Szinai, David Yates, Pedro A. Sánchez-Pérez, Martin Staadecker, Daniel M. Kammen, Andrew D. Jones, Patricia Hidalgo-Gonzalez
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-14 (2024)
Abstract The electric sector simultaneously faces two challenges: decarbonization to mitigate, and adaptation to manage, the impacts of climate change. In many regions, these challenges are compounded by an interdependence of electricity and water sy
Externí odkaz:
https://doaj.org/article/67c3deb16755449a9197ca316ba4df75
Autor:
Martin Staadecker, Julia Szinai, Pedro A. Sánchez-Pérez, Sarah Kurtz, Patricia Hidalgo-Gonzalez
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-15 (2024)
Abstract Long-duration energy storage (LDES) is a key resource in enabling zero-emissions electricity grids but its role within different types of grids is not well understood. Using the Switch capacity expansion model, we model a zero-emissions West
Externí odkaz:
https://doaj.org/article/c1f243950a664a7899546819f0eae256
Autor:
Feng, Chao, Celdran, Alberto Huertas, Sanchez, Pedro Miguel Sanchez, Kreischer, Jan, von der Assen, Jan, Bovet, Gerome, Perez, Gregorio Martinez, Stiller, Burkhard
Recent research has shown that the integration of Reinforcement Learning (RL) with Moving Target Defense (MTD) can enhance cybersecurity in Internet-of-Things (IoT) devices. Nevertheless, the practicality of existing work is hindered by data privacy
Externí odkaz:
http://arxiv.org/abs/2308.05978
Autor:
Gómez, Ángel Luis Perales, Beltrán, Enrique Tomás Martínez, Sánchez, Pedro Miguel Sánchez, Celdrán, Alberto Huertas
Industry 4.0 has brought numerous advantages, such as increasing productivity through automation. However, it also presents major cybersecurity issues such as cyberattacks affecting industrial processes. Federated Learning (FL) combined with time-ser
Externí odkaz:
http://arxiv.org/abs/2308.03554
Autor:
Beltrán, Enrique Tomás Martínez, Sánchez, Pedro Miguel Sánchez, Bernal, Sergio López, Bovet, Gérôme, Pérez, Manuel Gil, Pérez, Gregorio Martínez, Celdrán, Alberto Huertas
The rise of Decentralized Federated Learning (DFL) has enabled the training of machine learning models across federated participants, fostering decentralized model aggregation and reducing dependence on a server. However, this approach introduces uni
Externí odkaz:
http://arxiv.org/abs/2307.11730