Federated Deep Subspace Clustering
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913653017542656 |
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| author | Zhang, Yupei Feng, Ruojia Wang, Yifei Shang, Xuequn |
| author_facet | Zhang, Yupei Feng, Ruojia Wang, Yifei Shang, Xuequn |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_00230 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Federated Deep Subspace Clustering Zhang, Yupei Feng, Ruojia Wang, Yifei Shang, Xuequn Machine Learning Artificial Intelligence Cryptography and Security 68T07 I.5.3 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. |
| title | Federated Deep Subspace Clustering |
| topic | Machine Learning Artificial Intelligence Cryptography and Security 68T07 I.5.3 |
| url | https://arxiv.org/abs/2501.00230 |