SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning
Bibliographic record
Abstract
Federated Learning (FL) enables collaborative model training across distributed clients while preserving data privacy. However, conventional FL approaches often struggle to deliver accurate and personalized models in the presence of non-IID data. Although model pruning has been proposed to improve model adaptability, existing methods relying solely on local data often yield sub-optimal sub-models due to limited task-specific information. To address this, we propose SAFL (Structure-Aware Federated Learning), a novel framework that enhances personalization by integrating client clustering with Similar Client Structure Information (SCSI)-guided pruning. SAFL adopts a two-stage process: it first clusters clients based on data similarity and uses aggregated structural insights to guide pruning; then, clients train the resulting sub-models and participate in heterogeneous model aggregation. Extensive experiments on benchmark datasets demonstrate that SAFL achieves superior accuracy and model compactness compared to existing methods, particularly under non-IID settings. These results highlight the effectiveness of structure-aware pruning and collaboration in advancing personalized federated learning.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".