Abstract: Broadly speaking, embodiments of the present techniques provide a method and system for providing personal machine learning, ML, models. In particular, the present application provides a system for developing a training personal and personalised models to improve user experience.

Abstract: A method of obtaining a patched signal for training a model for use in at least one of a speech and an audio recognition is disclosed. The method comprises obtaining a first signal, wherein the first signal is at least one of a speech and an audio signal, modifying the first signal to obtain at least one second signal, dividing the first signal and the at least one second signal respectively into a plurality of first patches and a plurality of second patches, wherein each one of the plurality of first patches comprises a respective part of the first signal and each one of the plurality of second patches comprises a respective part of the at least one second signal and mixing selected ones of the plurality of first patches and the plurality of second patches to obtain a patched signal.

Abstract: A computer-implemented federated learning method is disclosed. The method comprises: for each of a number, n, of clients: determining a diversity score of a dataset corresponding to that client for training a machine learning model, wherein the diversity score is a measure of dataset variability; aggregating, weighted by the respective diversity score, models corresponding to each of the clients; and sending the aggregated model to at least one receiving client.

Abstract: The present techniques generally relate to a system and method for knowledge distillation between machine learning, ML, models. In particular, the present application relates to a computer-implemented method for training a condenser model to learn how to transfer knowledge between a teacher model and a student model, and using this trained condenser model to more quickly generate new student models.

Abstract: The present application generally relates to a method for training a machine learning, ML, model using class incremental learning, and to a computer-implemented method and apparatus for using the trained machine learning, ML, model. The method may learn how to update semantic representations of old concepts (classes) by modelling drift of semantic representations.