Privacy-Preserving Federated Learning Model for Healthcare Data
Bibliographic record
Abstract
Federated Machine Learning (FL) can be used effectively in distributed datasets, where data owners hesitate to share their raw data, as a reliable approach to train an ML algorithm. However, in the case of sensitive healthcare datasets, additional privacy measures before feeding into machine learning mechanisms are also necessary. Our approach uses the federated learning framework, which removes the necessity of sharing patients' sensitive data in a raw format outside the premise. First, the data owners agree on a list of features selected by the correlation; then, after training the local models, the obtained local models are transmitted to the central server for aggregation. The differential privacy (DP) approach is adopted to perturb the local models before transmission to add an extra privacy layer. As a result, our framework achieves improved utility as the feature selection reduces the data dimension. Finally, based on the patient's genomic data, the framework establishes a practical healthcare application to privacy-predict certain heart failure/cancer diseases. application to predict certain heart failure diseases in a private manner.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".