Fed-ensemble: Enhancing Federated Learning with Ensemble Models for an Explainable Thyroid Cancer Recurrence Prediction
Bibliographic record
Abstract
The prediction of thyroid cancer recurrence is a critical task in clinical decision-making, yet traditional machine learning models face significant challenges, particularly around data privacy, model generalization with huge datasets, and interpretability. In healthcare, patient data is sensitive and sharing it across institutions for model training raises privacy concerns. This research addresses these issues by utilizing federated learning (FL), a decentralized machine learning approach that allows institutions to collaboratively train a model while ensuring patient data remains private. FL enables local model training at each institution, with only model updates shared across participants, safeguarding sensitive data. Alongside federated learning, the study incorporates Explainable AI (XAI) techniques to enhance the transparency of predictions, enabling clinicians to interpret and trust the model’s decision-making process. By combining multiple machine learning models in an ensemble approach, the research improves the prediction accuracy and robustness of thyroid cancer recurrence, even with limited data. The method is evaluated using a cohort dataset of thyroid cancer patients, with synthetic data augmentation addressing data scarcity. The results demonstrate that the approach outperforms traditional models while addressing critical challenges of data privacy and model interpretability. This proposed model outperforms previous methods, significantly higher than the best result on same dataset from prior work. Additionally, our model shows superior performance even when trained on larger datasets, confirming its generalizability and robustness.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".