Today we live in a hyper-connected world, where huge amounts of data are produced on a daily basis by billions of devices. Within such a scenario, developing algorithms capable of working on edge devices while preserving the privacy of the users has become of the utmost importance. Federated Learning (FL) is a novel machine learning field of study born to address data privacy, data security and data access issues. Its innovation lies in proposing a framework in which it is possible to exploit privacy-protected data, without breaking any regulation. Data is accessed only locally, while only the model is exchanged among the devices (i.e.