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.

Artificial intelligence applications in healthcare are increasing every day. These applications have the ability to advance the healthcare industry by, for instance, supporting clinical decision making, risk prediction, developing early warning systems for patients, increasing the accuracy and timeliness of diagnosis, improving patient-physician interaction, and optimizing operations and resource allocation [21]. Federated learning is a new approach for distributed artificial intelligence that aims to have several agents train a deep learning model in a collaborative and secure way, without sharing any private data. This training is done the following way: a central server defines a deep learning model and sends it to the agents, who train the model in their private data. Then, they send the parameters of the model (weights or gradients) back to the server, who aggregates these data in order to find a global federated model, which in turn is delivered back to the agents to be retrained in their data. This process is iterated until convergence. In the initial definition of the federated learning approach, the aggregation step is done by averaging the model parameters. Nevertheless, other aggregation methods may be of more interest since they can improve the performance of the model by giving more weight to different agents depending on their size or the performance of the local models in their data.