An apparatus, method and computer program is described comprising receiving, at an edge device from one or more federated learning (FL) services, one or more FL machine learning (ML) models and capabilities information associated with each FL ML model, computing the utility of each FL ML model based a quality of available training samples at the edge device for training the corresponding FL ML models and said capabilities information, ranking the FL ML models in a descending order of utility of the FL ML models; and performing training of each of the corresponding FL ML models of each FL service in a descending order of utility until a remaining available cost budget of a total available cost budget of the edge device expires.