Modelling Audiological Preferences using Federated Learning

Patient-centric adaptation of audiological preferences across different contexts is a challenging task, as traditional clinical measurements of audibility do not reflect the cognitive perception of speech nor binaural loudness of sounds in different contexts. Smartphone-based machine learning personalization systems have the potential to address this issue in real-world listening scenarios, however, the necessary training datasets are not currently available. As hearing healthcare medical data is of a highly private nature, a framework is proposed, combining federated learning (FL) and secret sharing in the context of hearing aids with the goal of training models locally while preserving the individual user's privacy. We demonstrate an application of such a system with a simplified domain defined by the MNIST digit classification task.