Updating models with trained model update objects

Abstract

Some devices may perform processing using machine learning models trained at a centralized system and distributed to the device. The centralized system may update the machine learning model and distribute the update to the device (or devices). To reduce the size of an update, the centralized system may train a model update object, which may be smaller in size than the model itself and thus more suitable for sending to the device(s). A device may receive the model update object and use it to update the on-device machine learning model; for example, by changing some parameters of the model. Parameters left unchanged during the update may retain their previous value. Thus, using the model update object to update the on-device model may result in a more accurate updated model when compared to sending an updated model compressed to a size similar to that of the model update object.