F3 KM: Federated, Fair, and Fast k-means
Summary: Federated, fair, and fast k-means (F3 KM): decomposes fair clustering into client-local ADMM subproblems with only updates exchanged. Supports multiple or no sensitive attributes; linear-time computation and favorable communication trade-offs; scales to 5M points in ~1 hour. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Shengkun Zhu
- 2. Quanqing Xu
- 3. Jinshan Zeng
- 4. Sheng Wang
- 5. Yuan Sun
- 6. Zhifeng Yang
- 7. Chuanhui Yang
- 8. Zhiyong Peng
Incoming Citations (Sorted by Pagerank)
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,317 | Highly-Efficient Large-Scale k-means with Individual Fairness | 2026 | VLDB | 4.1945683e-05 |
| 10,716 | Federated and Balanced Clustering for High-dimensional Data | 2025 | VLDB | 4.1945683e-05 |
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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