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of 281
pro vyhledávání: '"Rodríguez-Barroso"'
At the same time that artificial intelligence is becoming popular, concern and the need for regulation is growing, including among other requirements the data privacy. In this context, Federated Learning is proposed as a solution to data privacy conc
Externí odkaz:
http://arxiv.org/abs/2410.08244
Autor:
Herrera, Francisco, Jiménez-López, Daniel, Argente-Garrido, Alberto, Rodríguez-Barroso, Nuria, Zuheros, Cristina, Aguilera-Martos, Ignacio, Bello, Beatriz, García-Márquez, Mario, Luzón, M. Victoria
In the realm of Artificial Intelligence (AI), the need for privacy and security in data processing has become paramount. As AI applications continue to expand, the collection and handling of sensitive data raise concerns about individual privacy prot
Externí odkaz:
http://arxiv.org/abs/2404.06127
Differential Privacy (DP) is a key property to protect data and models from integrity attacks. In the Deep Learning (DL) field, it is commonly implemented through the Differentially Private Stochastic Gradient Descent (DP-SGD). However, when a model
Externí odkaz:
http://arxiv.org/abs/2311.11717
Autor:
Vezhenkova Irina, Semenova Margarita, Kovalevskaya Alla, Gryaznov Artem, Rodríguez-Barroso M. Rocio, Jimenez Castañeda Rafael
Publikováno v:
E3S Web of Conferences, Vol 220, p 01057 (2020)
By 2050, according to the conclusion of the European Commission, the amount of solar panels waste will reach 78 million tons. 85% of all solar panels produced today belong to polycrystalline solar panels. The subject of this paper is the polymer comp
Externí odkaz:
https://doaj.org/article/bd3346bea3c44da89640c55ad0ac4e34
Autor:
Martín-García, Ana Pilar, Egea-Corbacho, Ágata, Franco, Ana Amelia, Rodríguez-Barroso, Rocío, Coello, María Dolores, Quiroga, José María
Publikováno v:
In Journal of Hazardous Materials Advances November 2024 16
Autor:
Rodríguez-Barroso, Nuria, López, Daniel Jiménez, Luzón, M. Victoria, Herrera, Francisco, Martínez-Cámara, Eugenio
Publikováno v:
Information Fusion (2022)
Federated learning is a machine learning paradigm that emerges as a solution to the privacy-preservation demands in artificial intelligence. As machine learning, federated learning is threatened by adversarial attacks against the integrity of the lea
Externí odkaz:
http://arxiv.org/abs/2201.08135
Publikováno v:
Future Generation Computer Systems, 133 (2022), 1-9
Federated learning, as a distributed learning that conducts the training on the local devices without accessing to the training data, is vulnerable to Byzatine poisoning adversarial attacks. We argue that the federated learning model has to avoid tho
Externí odkaz:
http://arxiv.org/abs/2007.15030
Autor:
Rodríguez-Barroso, Nuria, Stipcich, Goran, Jiménez-López, Daniel, Ruiz-Millán, José Antonio, Martínez-Cámara, Eugenio, González-Seco, Gerardo, Luzón, M. Victoria, Veganzones, Miguel Ángel, Herrera, Francisco
Publikováno v:
Information Fusion 64 (2020) 270-292
The high demand of artificial intelligence services at the edges that also preserve data privacy has pushed the research on novel machine learning paradigms that fit those requirements. Federated learning has the ambition to protect data privacy thro
Externí odkaz:
http://arxiv.org/abs/2007.00914
Autor:
Egea-Corbacho, Agata, Martín-García, Ana Pilar, Franco, Ana A., Albendín, Gemma, Arellano, Juana Mª, Rodríguez-Barroso, Rocío, Coello, Mª Dolores, Quiroga, José Mª., Cabello, Jose F., Iglesias Prado, Iria, Malta, Erik-jan
Publikováno v:
In Science of the Total Environment 15 December 2023 904
Autor:
Rodriguez-Barroso, Alejandro, Camacho, Guillermo, Martinez-Cano, Oscar, Rafael Morillas, Jose, de Vicente, Juan
Publikováno v:
In Measurement 30 November 2023 222