  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.
