Federated Deep Subspace Clustering

• URL: http://arxiv.org/abs/2501.00230v2
• Date: Thu, 16 Jan 2025 02:28:47 GMT
• Title: Federated Deep Subspace Clustering
• Authors: Yupei Zhang, Ruojia Feng, Yifei Wang, Xuequn Shang,
• Abstract summary: 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. Experiments test FDSC on public datasets and compare with other clustering methods, demonstrating the effectiveness of FDSC.
• Score: 14.279959858427778
• License:
• Abstract: 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.

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