Federated learning aims at training a machine learning algorithm, for instance deep neural networks, on multiple local datasets contained in local nodes without exchanging data samples. The general principle consists in training local models on local data samples and exchanging parameters (e.g. the weights of a deep neural network) between these local models at some frequency to generate a global model.