One of the defining characterist wwwics of federated learning is that it keeps raw data decentralised, train model decentralised and then aggregate. Unlike traditional data centre-based distributed learning settings where data is arbitrarily distributed and any node within the network can access the data, Federated Learning involves heterogeneous distributed data to help protect privacy.