Broadly speaking, the present techniques generally relates to methods, systems and apparatuses for training a machine learning (ML) model using federated learning. In particular, a method for training a machine learning (ML) model using federated learning performed by a plurality of client devices, the method comprising determining a computation capability of each client device, associating each client device with a value defining how much of each neural network layer of the ML model is to be included in a submodel to be trained by the each client device, based on the determined computation capability and generating a submodel of the ML model by using the value associated with the each client device to perform ordered pruning of at least one neural network layer of the ML model, is provided.