Federated learning is a technique which has been introduced to evolve machine learning and to provide a distributed learning structure more suited to be applied to complex environments such as IoT or privacy-preserving applications. The federated aggregation process is controlled by a parameter server, but, differently from centralised ML, an additional on-site step of local training is added in the devices; in this way, data is not disclosed and the communication overhead reduces significantly. Because of its distributed nature, FL faces a highly heterogeneous environment; clients differ in computational capabilities (especially considering IoT devices), datasets distributions (data is generated or collected on the device, and this may bias or pollute the population), reliability (clients may drop out mid-run).