  Federated Learning (FL) enables training state-of-the-art Automatic Speech
Recognition (ASR) models on user devices (clients) in distributed systems,
hence preventing transmission of raw user data to a central server. A key
challenge facing practical adoption of FL for ASR is obtaining ground-truth
labels on the clients. Existing approaches rely on clients to manually
transcribe their speech, which is impractical for obtaining large training
corpora. A promising alternative is using semi-/self-supervised learning
approaches to leverage unlabelled user data. To this end, we propose FedNST, a
novel method for training distributed ASR models using private and unlabelled
user data. We explore various facets of FedNST, such as training models with
different proportions of labelled and unlabelled data, and evaluate the
proposed approach on 1173 simulated clients. Evaluating FedNST on LibriSpeech,
where 960 hours of speech data is split equally into server (labelled) and
client (unlabelled) data, showed a 22.5% relative word error rate reduction}
(WERR) over a supervised baseline trained only on server data.
