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A Chaotic Maps-Based Privacy-Preserving Distributed Deep Learning for Incomplete and Non-IID Datasets
dc.contributor.author | Arévalo, Irina | |
dc.contributor.author | Salmeron, Jose L. | |
dc.date.accessioned | 2024-07-03T19:41:43Z | |
dc.date.available | 2024-07-03T19:41:43Z | |
dc.date.issued | 2024 | |
dc.identifier | 10.1109/TETC.2023.3320758 | |
dc.identifier.issn | 21686750 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12728/11564 | |
dc.description.abstract | 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 of their data. In this research, the authors employ a secured Federated Learning method with an additional layer of privacy and proposes a method for addressing the non-IID challenge. Moreover, differential privacy is compared with chaotic-based encryption as layer of privacy. The experimental approach assesses the performance of the federated deep learning model with differential privacy using both IID and non-IID data. In each experiment, the Federated Learning process improves the average performance metrics of the deep neural network, even in the case of non-IID data. © 2013 IEEE. | es_ES |
dc.language.iso | en | es_ES |
dc.publisher | IEEE Computer Society | es_ES |
dc.subject | Federated learning | es_ES |
dc.subject | non-IID datasets | es_ES |
dc.subject | privacy-preserving machine learning | es_ES |
dc.title | A Chaotic Maps-Based Privacy-Preserving Distributed Deep Learning for Incomplete and Non-IID Datasets | es_ES |
dc.type | Article | es_ES |