  Federated learning (FL) is a privacy-preserving machine learning method that
has been proposed to allow training of models using data from many different
clients, without these clients having to transfer all their data to a central
server. There has as yet been relatively little consideration of FL or other
privacy-preserving methods in audio. In this paper, we investigate using FL for
a sound event detection task using audio from the FSD50K dataset. Audio is
split into clients based on uploader metadata. This results in highly
imbalanced subsets of data between clients, noted as a key issue in FL
scenarios. A series of models is trained using `high-volume' clients that
contribute 100 audio clips or more, testing the effects of varying FL
parameters, followed by an additional model trained using all clients with no
minimum audio contribution. It is shown that FL models trained using the
high-volume clients can perform similarly to a centrally-trained model, though
there is much more noise in results than would typically be expected for a
centrally-trained model. The FL model trained using all clients has a
considerably reduced performance compared to the centrally-trained model.
