Listar Investigación y Desarrollo por autor "Arévalo, Irina"
Mostrando ítems 1-3 de 3
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Article
A Chaotic Maps-Based Privacy-Preserving Distributed Deep Learning for Incomplete and Non-IID Datasets (2024)
Arévalo, Irina; Salmeron, Jose L. (IEEE Computer Society, 2024)Federated Learning is a machine learning approach that enables the training of a deep learning model among several participants with sensitive data that wish to share their own knowledge without compromising the privacy ... -
Article
Benchmarking federated strategies in Peer-to-Peer Federated learning for biomedical data (2024)
Salmeron, Jose L.; Arévalo, Irina; Ruiz-Celma, Antonio (Elsevier Ltd, 2023)The increasing requirements for data protection and privacy have attracted a huge research interest on distributed artificial intelligence and specifically on federated learning, an emerging machine learning approach that ... -
Article
Blind Federated Learning without initial model (2024)
Salmeron, Jose L.; Arévalo, Irina (Springer Nature, 2024)Federated learning is an emerging machine learning approach that allows the construction of a model between several participants who hold their own private data. This method is secure and privacy-preserving, suitable for ...