A chaotic maps-based privacy-preserving distributed deep learning for incomplete and Non-IID datasets

• URL: http://arxiv.org/abs/2402.10145v1
• Date: Thu, 15 Feb 2024 17:49:50 GMT
• Title: A chaotic maps-based privacy-preserving distributed deep learning for incomplete and Non-IID datasets
• Authors: Irina Ar\'evalo and Jose L. Salmeron
• Abstract summary: Federated Learning is a machine learning approach that enables the training of a deep learning model among several participants with sensitive data. In this research, the authors employ a secured Federated Learning method with an additional layer of privacy and propose a method for addressing the non-IID challenge.
• Score: 1.30536490219656
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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