Present performance of machine learning systems—optimization of parameters, weights, biases—at least in part relies on large volumes of training data which, as any other competitive asset, is dispersed, distributed, or maintained by various R&D and business data owners, rather than being stored by a single central entity. Collaboratively training a machine learning (ML) model on such distributed data—federated learning, or FL—can result in a more accurate and robust model than any participant could train in isolation.