mlsanityData to 7 Oct 2026

Federated Momentum Contrastive Clustering

arXiv 2206.05093 ↗First posted 10 Jun 2022cs.LG cs.CV stat.MLcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 43 of its 66 references that are in the map. First posted on arXiv 10 Jun 2022.

Builds on

43 of 66 references.

References · 43

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31 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 1; 2026: 1.

6 papers.

  1. 2024Adaptive Aggregation Weights for Federated Segmentation of Pancreas MRIcited by 5
  2. 2024CCFC: Bridging Federated Clustering and Contrastive Learningcited by 3
  3. 2023Multichannel Orthogonal Transform-Based Perceptron Layers for Efficient ResNetscited by 2
  4. 2024IPMN Risk Assessment under Federated Learning Paradigmcited by 2
  5. 2025Federated Breast Cancer Detection Enhanced by Synthetic Ultrasound Image Augmentationcited by 0
  6. 2026Federated Deep Clustering Networks for High-Dimensional and Heterogeneous Datacited by 0