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Federated K-means Clustering
Published 2 Oct 2023 in cs.LG and cs.DC | (2310.01195v2)
Abstract: Federated learning is a technique that enables the use of distributed datasets for machine learning purposes without requiring data to be pooled, thereby better preserving privacy and ownership of the data. While supervised FL research has grown substantially over the last years, unsupervised FL methods remain scarce. This work introduces an algorithm which implements K-means clustering in a federated manner, addressing the challenges of varying number of clusters between centers, as well as convergence on less separable datasets.
- “k-means++: The Advantages of Careful Seeding”, 2006
- “LEAF: A Benchmark for Federated Settings” In CoRR abs/1812.01097, 2018 arXiv: http://arxiv.org/abs/1812.01097
- “EMNIST: an extension of MNIST to handwritten letters” In CoRR abs/1702.05373, 2017 arXiv: http://arxiv.org/abs/1702.05373
- Don Kurian Dennis, Tian Li and Virginia Smith “Heterogeneity for the Win: One-Shot Federated Clustering”, 2021
- “Algorithm AS 136: A K-Means Clustering Algorithm” In Journal of the Royal Statistical Society. Series C (Applied Statistics) 28, 1979, pp. 100–108
- “Multi-Party Verifiable Privacy-Preserving Federated k-Means Clustering in Outsourced Environment”, 2021 DOI: 10.1155/2021/3630312
- E.R. Hruschka, L.N. Castro and R.J.G.B. Campello “Evolutionary algorithms for clustering gene-expression data” In Fourth IEEE International Conference on Data Mining (ICDM’04), 2004, pp. 403–406 DOI: 10.1109/ICDM.2004.10073
- Hemant H Kumar, V R Karthik and Mydhili K Nair “Federated K-Means Clustering: A Novel Edge AI Based Approach for Privacy Preservation; Federated K-Means Clustering: A Novel Edge AI Based Approach for Privacy Preservation” In 2020 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM), 2020 DOI: 10.1109/CCEM50674.2020.00021
- Geun Hyeong Lee and Soo Yong Shin “Federated Learning on Clinical Benchmark Data: Performance Assessment” In Journal of Medical Internet Research 22.10 JMIR Publications Inc., 2020 DOI: 10.2196/20891
- “A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection — Enhanced Reader” In IEEE Transactions on Knowledge and Data Engineering, 2021, pp. 1–1 DOI: 10.1109/TKDE.2021.3124599
- “Federated Learning: Challenges, methods and future directions”, 2020 DOI: 10.1109/MSP.2020.2975749
- “Privacy-preserving federated k-means for proactive caching in next generation cellular networks” In Information Sciences 521 Elsevier Inc., 2020, pp. 14–31 DOI: 10.1016/J.INS.2020.02.042
- “Communication-Efficient Learning of Deep Networks from Decentralized Data”, 2017, pp. 10
- “Privacy-first health research with federated learning.” In NPJ digital medicine 4.1 Nature Research, 2021, pp. 132 DOI: 10.1038/s41746-021-00489-2
- Mykola Servetnyk, Carrson C Fung and Zhu Han “Unsupervised Federated Learning for Unbalanced Data; Unsupervised Federated Learning for Unbalanced Data” In GLOBECOM 2020 - 2020 IEEE Global Communications Conference, 2020 DOI: 10.1109/GLOBECOM42002.2020.9348203
- “An Analysis of the Application of Simplified Silhouette to the Evaluation of k-means Clustering Validity” In Machine Learning and Data Mining in Pattern Recognition Cham: Springer International Publishing, 2017, pp. 291–305
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