Federated and Balanced Clustering for High-dimensional Data
Summary: Teb-means recasts balanced k-means as trace maximization with coordinatewise optimization, decomposed across vertical-federated parties without raw-data sharing. Greedy block updates achieve linear client time and constant communication rounds, yielding 12.18× speedups. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Yushuai Ji (Wuhan University)
- 2. Shengkun Zhu (Wuhan University)
- 3. Shixun Huang (University of Wollongong)
- 4. Zepeng Liu (Wuhan University)
- 5. Sheng Wang (Wuhan University)
- 6. Zhiyong Peng (Wuhan University)
BibTeX Citation
@article{ji_vldb25,
title = {{Federated and Balanced Clustering for High-dimensional Data}},
author = {Ji, Yushuai and Zhu, Shengkun and Huang, Shixun and Liu, Zepeng and Wang, Sheng and Peng, Zhiyong},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {11},
pages = {4032--4044},
doi = {10.14778/3749646.3749673},
url = {https://doi.org/10.14778/3749646.3749673},
year = {2025}
}
Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 11 of 11 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 4,697 | Multi-Dimensional Balanced Graph Partitioning via Projected Gradient Descent | 2019 | VLDB |
| 2 | 10,869 | Federated Data Distribution Shift Estimation | 2025 | VLDB |
| 3 | 8,917 | A Blockchain System for Clustered Federated Learning with Peer-to-Peer Knowledge Transfer | 2024 | VLDB |
| 4 | 8,045 | Contributions Estimation in Federated Learning: A Comprehensive Experimental Evaluation | 2024 | VLDB |
| 5 | 11,184 | Settling Time vs. Accuracy Tradeoffs for Clustering Big Data | 2024 | SIGMOD |
| 6 | 5,314 | Optimal Differentially Private Algorithms for k-Means Clustering | 2018 | PODS |
| 7 | 10,615 | Highly-Efficient Large-Scale k-means with Individual Fairness | 2026 | VLDB |
| 8 | 11,161 | Efficient Algorithm for K-Multiple-Means | 2024 | SIGMOD |
| 9 | 6,353 | Differentially Private Vertical Federated Clustering | 2023 | VLDB |
| 10 | 11,420 | F3 KM: Federated, Fair, and Fast k-means | 2023 | SIGMOD |