Analysis and Tuning of Knowledge Distillation for Efficient Collaborative Learning
Why this work is in the frame
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Bibliographic record
Abstract
Knowledge Distillation (KD) has become a crucial technique for efficient collaborative learning in distributed systems, especially under data and system heterogeneity. Despite considerable attention, few works systematically examine tuning strategies for KD hyperparameters or provide comprehensive empirical comparisons across different data distributions. In this paper, we conduct an in-depth study of multiple KD methods-Vanilla KD, Deep Mutual Learning (DML), Data Partitioning KD (DP-KD), and our Tuned KD approach-using diverse data partitions and transfer sets. We show that (1) hyperparameter tuning dramatically boosts KD performance, especially when teacher-student models have disparate accuracies, (2) different transfer set sizes and labeling conditions substantially affect outcomes. Our findings serve as practical guidelines for effectively applying KD to collaborative learning tasks.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it