Recently, Machine Learning and Deep Learning have become the state-of-theart techniques in different domains, such as computer vision, natural language processing and speech recognition. One of the main drawbacks is the necessity of a huge amount of data to be trained correctly. So far, the traditional way consists in gathering samples at only one central infrastructure, which trains the model. However, if data are recorded by different devices, this process presents two main disadvantages: first, transmitting all of them requires a great consumption of network resources and, second, it can expose sensitive information. In such a context, a new procedure has emerged: Federated Learning.