  Unsupervised clustering under domain shift (UCDS) studies how to transfer the
knowledge from abundant unlabeled data from multiple source domains to learn
the representation of the unlabeled data in a target domain. In this paper, we
introduce Prototype-oriented Clustering with Distillation (PCD) to not only
improve the performance and applicability of existing methods for UCDS, but
also address the concerns on protecting the privacy of both the data and model
of the source domains. PCD first constructs a source clustering model by
aligning the distributions of prototypes and data. It then distills the
knowledge to the target model through cluster labels provided by the source
model while simultaneously clustering the target data. Finally, it refines the
target model on the target domain data without guidance from the source model.
Experiments across multiple benchmarks show the effectiveness and
generalizability of our source-private clustering method.

Machine Learning;Cryptography and Security