  We propose FEDENHANCE, an unsupervised federated learning (FL) approach for
speech enhancement and separation with non-IID distributed data across multiple
clients. We simulate a real-world scenario where each client only has access to
a few noisy recordings from a limited and disjoint number of speakers (hence
non-IID). Each client trains their model in isolation using mixture invariant
training while periodically providing updates to a central server. Our
experiments show that our approach achieves competitive enhancement performance
compared to IID training on a single device and that we can further facilitate
the convergence speed and the overall performance using transfer learning on
the server-side. Moreover, we show that we can effectively combine updates from
clients trained locally with supervised and unsupervised losses. We also
release a new dataset LibriFSD50K and its creation recipe in order to
facilitate FL research for source separation problems.
