Decentralized Model Selection for Test-Time Adaptation in Heterogeneous Connected Systems
Bibliographic record
Abstract
Traditional centralized model training assumes that data samples are readily available and can be processed without constraints. In contrast, decentralized machine learning (DML) addresses the limitation by collaborative model training and inference directly on distributed data sources. The transformation from data centralization to decentralization helps comply with data regulations and improves system scalability with reduced reliance on cloud servers. However, a tradeoff between model personalization and generalization exists: the fine-tuning of local training data distribution sacrifices model generalization on the testing data distribution that differs from the training data distribution. To improve the tradeoff, we propose a DML framework that can inherently make model personalization and generalization easier by selecting a model among multiple ones judiciously. We develop a scalable selector for model selection and use blockchain to achieve model consensus. The personalized model selector is then proposed for test-time adaptation. Using computer simulations, we show that our method not only outperforms competitive personalization benchmarks but also generalizes well for new data distributions with various shifts.
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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 | 0.010 |
| 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 | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".