Federated clustering, an essential extension of centralized clustering for
federated scenarios, enables multiple data-holding clients to collaboratively
group data while keeping their data locally. In centralized scenarios,
clustering driven by representation learning has made significant advancements
in handling high-dimensional complex data. However, the combination of
federated clustering and representation learning remains underexplored. To
bridge this, we first tailor a cluster-contrastive model for learning
clustering-friendly representations. Then, we harness this model as the
foundation for proposing a new federated clustering method, named
cluster-contrastive federated clustering (CCFC). Benefiting from representation
learning, the clustering performance of CCFC even double those of the best
baseline methods in some cases. Compared to the most related baseline, the
benefit results in substantial NMI score improvements of up to 0.4155 on the
most conspicuous case. Moreover, CCFC also shows superior performance in
handling device failures from a practical viewpoint.

通过使用集群对比模型来学习友好的聚类表示，并以此为基础，提出了一种新的联邦聚类方法，称为集群对比联邦聚类（CCFC），其聚类性能甚至在某些情况下是最佳基准方法的两倍，并且在处理设备故障方面也显示出卓越的性能。