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Federated Deep Subspace Clustering

arXiv 2501.00230 ↗First posted 31 Dec 2024cs.LG cs.AI cs.CRcited by 0

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Abstract (arXiv)

This paper introduces FDSC, a private-protected subspace clustering (SC) approach with federated learning (FC) schema. In each client, there is a deep subspace clustering network accounting for grouping the isolated data, composed of a encode network, a self-expressive layer, and a decode network. FDSC is achieved by uploading the encode network to communicate with other clients in the server. Besides, FDSC is also enhanced by preserving the local neighborhood relationship in each client. With the effects of federated learning and locality preservation, the learned data features from the encoder are boosted so as to enhance the self-expressiveness learning and result in better clustering performance. Experiments test FDSC on public datasets and compare with other clustering methods, demonstrating the effectiveness of FDSC.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 25 of its 59 references that are in the map. First posted on arXiv 31 Dec 2024.

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References · 25

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13 more
  1. 2017Deep Subspace Clustering Networkscited by 94
  2. 2021Heterogeneity for the Win: One-Shot Federated Clusteringcited by 87
  3. 2022Orchestra: Unsupervised Federated Learning via Globally Consistent Clusteringcited by 45
  4. 2022FedX: Unsupervised Federated Learning with Cross Knowledge Distillationcited by 40
  5. 2018Deep Multimodal Subspace Clustering Networkscited by 31
  6. 2022Towards Federated Clustering: A Federated Fuzzy c-Means Algorithm (FFCM)cited by 30
  7. 2019Multi-view Deep Subspace Clustering Networkscited by 24
  8. 2020A Critique of Self-Expressive Deep Subspace Clusteringcited by 18
  9. 2020Stochastic Sparse Subspace Clusteringcited by 17
  10. 2024Asynchronous Federated Clustering with Unknown Number of Clusterscited by 11
  11. 2020Privacy Threats Against Federated Matrix Factorizationcited by 6
  12. 2019Residual Encoder-Decoder Network for Deep Subspace Clusteringcited by 4
  13. 2023Federated learning-outcome prediction with multi-layer privacy protectioncited by 2

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