One-Shot Federated Clustering Based on Stable Distance Relationships
Bibliographic record
Abstract
Federated clustering (FC) is an emerging and important topic in data clustering research. However, for existing works, there are two challenging issues as follows. 1) FC does not perform well on non-IID data. 2) Differential privacy is a common-used way to protect raw data in FC, but there is no solid theoretical basis for selecting privacy budget$\epsilon$in Laplacian noise, and$\epsilon$is randomly set in most algorithms. In this article, we propose a new framework called NN-FC for addressing the above-mentioned issues. Specifically, 1) we provide a rigorous mathematical proof when selecting$\epsilon$, we have shown that when the value of$\epsilon$satisfies certain conditions, the neighbor relationship of data points before and after adding Laplacian noises remains unchanged. 2) According to 1), we propose a new method of obtaining global cluster centers based on distance relationships at the server, and the results of clustering the original data and clustering the privacy data become close. The experimental results show that NN-FC performs better than eight traditional and state-of-the-art (SOTA) centralized (nonfederated) clustering algorithms. In particular, NN-FC performs better than two SOTA FC frameworks k-FED (ICML2021) and MUFC (ICLR2023).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".