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 learning (FedUL), where the unlabeled data
are transformed into surrogate labeled data for each of the clients, a modified
model is trained by supervised FL, and the wanted model is recovered from the
modified model. FedUL is a very general solution to unsupervised FL: it is
compatible with many supervised FL methods, and the recovery of the wanted
model can be theoretically guaranteed as if the data have been labeled.
Experiments on benchmark and real-world datasets demonstrate the effectiveness
of FedUL. Code is available at this https URL

本文介绍未监督联邦学习，通过共享客户拥有的未标记数据的条件分布，移动类先验概率，并将客户拥有的未标记数据转换为每个客户的替代标记数据，从而在联邦学习中实现模型的共享，即联邦未监督学习（FedUL）模型。FedUL 是通用的未监督联邦学习解决方案，可兼容许多监督 FL 方法，实验结果表明了 FedUL 的有效性。