Federated Learning, also known as Collaborative Learning, is a relatively new Machine Learning field of study, born in 2015 to address critical matters such as data privacy, data security and data access. In this scenario, a central server model is trained exploiting data stored locally on multiple devices (i.e. the clients). Unlike the standard machine learning setting, here the model has no direct access to the data themselves: a fundamental requirement for any application where the user's privacy must be preserved (e.g. medical records, bank transactions). The server model is asynchronously sent to the clients which train it using their own local data.