Supervised federated learning (FL) enables multiple clients to share the trained model
without sharing their labeled data. However, potential clients might even be reluctant to label
their own data, which could limit the applicability of FL in practice. In this paper, we show the
possibility of unsupervised FL whose model is still a classifier for predicting class labels, if
the class-prior probabilities are shifted while the class-conditional distributions are shared
among the unlabeled data owned by the clients. We propose federation of unsupervised …