An Adaptive Federated Fuzzy C-Means Clustering With Nonindependently and Identically Distributed Data
Bibliographic record
Abstract
Federated Fuzzy C-Means (FCM) has received considerable attention due to the increasing need for privacy-conscious data analysis across diverse domains and sources in many real-world applications. Recent developments in federated FCM, however, are still in their infancy and largely unexplored. These methods struggle to handle nonindependent and identically distributed (non-iid) data. Moreover, critical hyperparameters, such as the number of iterations for local updates, are typically set manually, which can significantly affect the performance of federated clustering. To address these challenges, we introduce an Adaptive Federated FCM with an auxiliary model, named AF-FCM. In this approach, prior information from the auxiliary model, along with a proximal term in the local objective, mitigates the effects of the non-iid environment, enhancing both model robustness and effectiveness. Critical hyperparameters are adaptively adjusted using a proposed adaptive particle swarm optimization (APSO) algorithm, guided by a carefully designed fitness function. Within APSO, a nonlinear regression function adjusts the inertia weight, reducing the risk of convergence to local optima. In AF-FCM, global prototypes are refined using momentum gradient descent (MGD). Numerical experiments highlight the effectiveness and generalization performance of AF-FCM across various conditions, including heterogeneity variations, the number of clients, and the number of clusters. Comparative analysis against state-of-the-art federated clustering baseline methods validates the competitive performance of AF-FCM.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".