  We present federated momentum contrastive clustering (FedMCC), a learning
framework that can not only extract discriminative representations over
distributed local data but also perform data clustering. In FedMCC, a
transformed data pair passes through both the online and target networks,
resulting in four representations over which the losses are determined. The
resulting high-quality representations generated by FedMCC can outperform
several existing self-supervised learning methods for linear evaluation and
semi-supervised learning tasks. FedMCC can easily be adapted to ordinary
centralized clustering through what we call momentum contrastive clustering
(MCC). We show that MCC achieves state-of-the-art clustering accuracy results
in certain datasets such as STL-10 and ImageNet-10. We also present a method to
reduce the memory footprint of our clustering schemes.
