In a FL system, there is no assumption regarding the distribution of the data within each participant’s dataset, nor assumptions regarding the size of each of the distributed datasets. This flexibility is another key advantage of the technique. It allows different participants, with different volumes or distribution of data and varying capabilities, to collaborate on the training of ML models. Having heterogeneous datasets potentially helps building models that can generalise better. Each participant can then benefit from models trained on a richer and broader range of data with no additional cost for acquiring this data.