The unceasing development of Machine Learning (ML) and the evolution of DeepLearning have revolutionized many application domains, ranging from natural language processing, to video analytics, to biology and medical predictions. The most common approach for ML models training is cloud-centric, so data owners transmit the training data to a public cloud server for processing, where resides more powerful resources. However, this approach is often unfeasible due to privacy laws and restrictions, as well as the burdening of network communications because of the massive quantities of data that need to be transmitted to a distant cloud server. To solve these problems, Google introduced in 2016 the concept of Federated Learning (FL) with the objective of building machine learning models that takes into account security and privacy of data.