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.

翻译：联邦学习是一种机器学习方法，它允许多个拥有敏感数据的参与者在不损害数据隐私的前提下，通过共享各自知识来训练深度学习模型。本研究采用了一种具有额外隐私层的安全联邦学习方法，并提出了一种解决非独立同分布挑战的方法。此外，研究将差分隐私与基于混沌的加密作为隐私层进行了比较。实验方法评估了使用差分隐私的联邦深度学习模型在独立同分布和非独立同分布数据上的性能。在每次实验中，联邦学习过程都提高了深度神经网络的平均性能指标，即使在非独立同分布数据情况下也是如此。