Privacy-Preserving Federated Learning Model for Healthcare Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.024 | 0.140 |
| Research integrity | 0.000 | 0.001 |
| 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