A chaotic maps-based privacy-preserving distributed deep learning for incomplete and Non-IID datasets
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866913235185172480 |
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| author | Arévalo, Irina Salmeron, Jose L. |
| author_facet | Arévalo, Irina Salmeron, Jose L. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_10145 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A chaotic maps-based privacy-preserving distributed deep learning for incomplete and Non-IID datasets Arévalo, Irina Salmeron, Jose L. Machine Learning Cryptography and Security Distributed, Parallel, and Cluster Computing 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. |
| title | A chaotic maps-based privacy-preserving distributed deep learning for incomplete and Non-IID datasets |
| topic | Machine Learning Cryptography and Security Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2402.10145 |