基于混沌图的不完整和非 IID 数据集隐私保护分布式深度学习

IF 5.1 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS IEEE Transactions on Emerging Topics in Computing Pub Date : 2023-10-05 DOI:10.1109/TETC.2023.3320758
Irina Arévalo;Jose L. Salmeron
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引用次数: 0

摘要

Federated Learning 是一种机器学习方法,它可以在多个拥有敏感数据的参与者之间训练深度学习模型,这些参与者希望在不损害其数据隐私的情况下分享自己的知识。在这项研究中,作者采用了一种具有额外隐私层的安全联邦学习方法,并提出了一种应对非 IID 挑战的方法。此外,还将差分隐私与混沌加密作为隐私层进行了比较。实验方法使用 IID 和非 IID 数据评估了具有差分隐私的联合深度学习模型的性能。在每个实验中,联合学习过程都提高了深度神经网络的平均性能指标,即使在非 IID 数据的情况下也是如此。
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A Chaotic Maps-Based Privacy-Preserving Distributed Deep Learning for Incomplete and Non-IID Datasets
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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来源期刊
IEEE Transactions on Emerging Topics in Computing
IEEE Transactions on Emerging Topics in Computing Computer Science-Computer Science (miscellaneous)
CiteScore
12.10
自引率
5.10%
发文量
113
期刊介绍: IEEE Transactions on Emerging Topics in Computing publishes papers on emerging aspects of computer science, computing technology, and computing applications not currently covered by other IEEE Computer Society Transactions. Some examples of emerging topics in computing include: IT for Green, Synthetic and organic computing structures and systems, Advanced analytics, Social/occupational computing, Location-based/client computer systems, Morphic computer design, Electronic game systems, & Health-care IT.
期刊最新文献
Table of Contents Front Cover IEEE Transactions on Emerging Topics in Computing Information for Authors Special Section on Emerging Social Computing DALTON - Deep Local Learning in SNNs via local Weights and Surrogate-Derivative Transfer
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