Federated Learning (FL) has recently received a lot of attention in academic and industry research, since it enables different devices, or clients, to collaboratively train a shared model while keeping their local data private. This paradigm allows for the participation of data that would otherwise be unavailable in the training phase due to privacy concerns. Furthermore, FL also comes in handy when the sheer volume of data involved in the training phase is too large for a single data storage to handle. While enabling learning from privacy-protected data comes with many benefits, this paradigm also introduces some challenges: the first one is the cost of communication between the server, which manages all training operations, and the clients; in addition, the heterogeneity that may exist among the various clients participating in the training phase may degrade FL algorithms performance and significantly slow down convergence.