FedDeepInsight—A privacy-first federated learning architecture for medical 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
Medical data, hospital patient-specific data, are highly sensitive to privacy and are essential for research in the biomedical field. Although there are many new approaches to creating databases that ensure data must be FAIR and GDPR compliant, these approaches require the intervention of secured data handlers. To address this gap, this study investigates and designs a standardized Federated Learning (FL) architecture for medical data. Specifically, we examine traditional and novel methods for preprocessing, handling, and utilizing such data in FL. We develop “FedDeepInsight”, a novel data transformation framework that enables tabular data augmentation and transformation into image data prior to neural network training and FL. Additionally, we analyze how the type of dataset influences the performance of federated learning algorithms and machine learning models in terms of accuracy and efficiency. Our results indicate that FedAvg is the most reliable aggregation algorithm, providing superior accuracy, stability, and convergence, and FedYogi is also viable with well-tuned hyperparameters. For privacy protection, we recommend Differential Privacy (DP) with calibrated noise multipliers and initial upper and lower bounds for stability. Ultimately, we emerge as a promising solution for secure, privacy-preserving federation learning in healthcare.
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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 | 0.003 | 0.161 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.041 | 0.072 |
| 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