  Federated Learning is a distributed machine learning paradigm dealing with
decentralized and personal datasets. Since data reside on devices like
smartphones and virtual assistants, labeling is entrusted to the clients, or
labels are extracted in an automated way. Specifically, in the case of audio
data, acquiring semantic annotations can be prohibitively expensive and
time-consuming. As a result, an abundance of audio data remains unlabeled and
unexploited on users' devices. Most existing federated learning approaches
focus on supervised learning without harnessing the unlabeled data. In this
work, we study the problem of semi-supervised learning of audio models via
self-training in conjunction with federated learning. We propose FedSTAR to
exploit large-scale on-device unlabeled data to improve the generalization of
audio recognition models. We further demonstrate that self-supervised
pre-trained models can accelerate the training of on-device models,
significantly improving convergence to within fewer training rounds. We conduct
experiments on diverse public audio classification datasets and investigate the
performance of our models under varying percentages of labeled and unlabeled
data. Notably, we show that with as little as 3% labeled data available,
FedSTAR on average can improve the recognition rate by 13.28% compared to the
fully supervised federated model.
