CCFC: Bridging Federated Clustering and Contrastive Learning
Jie Yan Jing Liu Zhong-Yuan Zhang
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.