A chaotic maps-based privacy-preserving distributed deep learning for incomplete and Non-IID datasets
<|reference_start|>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.<|reference_end|>
@article{arévalo2024a,
    title={A chaotic maps-based privacy-preserving distributed deep learning for
  incomplete and Non-IID datasets},
    author={Irina Ar'evalo and Jose L. Salmeron},
    journal={IEEE Transactions on Emerging Topics in Computing, 2023},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.10145},
    primaryClass={cs.LG cs.CR cs.DC}
}