mlsanityData to 14 Oct 2026

CCFC: Bridging Federated Clustering and Contrastive Learning

arXiv 2401.06634 ↗First posted 12 Jan 2024cs.LG cs.AIcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 37 of its 61 references that are in the map. First posted on arXiv 12 Jan 2024.

Builds on

37 of 61 references.

References · 37

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25 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 1; 2026: 1.

4 papers.

  1. 2024Training Machine Learning models at the Edge: A Surveycited by 11
  2. 2024CCFC++: Enhancing Federated Clustering through Feature Decorrelationcited by 3
  3. 2025OmniFC: Rethinking Federated Clustering via Lossless and Secure Distance Reconstructioncited by 0
  4. 2026Fed-BRDECS: Privacy-Preserving and Heterogeneity-Aware Federated Deep Embedded Clusteringcited by 0