Co-Training-Based Personalized Federated Learning With Generative Adversarial Networks for Enhanced Mobile Smart Healthcare Diagnosis
Why this work is in the frame
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Bibliographic record
Abstract
The widespread implementation of Artificial Itelligence (AI) has led to significant advancements in disease diagnosis. Personalized Federated Learning (FL) trains models tailored to each patient’s needs but often overlooks model architecture heterogeneity. We propose a novel Co-training-based personalized FL with Generative Adversarial Networks (GANs) for Smart Healthcare Diagnosis (CFG-SHD). This approach allows privacy-preserving participation in FL by enabling patients to keep their model architectures and parameters private. Key contributions include integrating co-training into FL for leveraging multiple data views and using GANs to generate synthetic data, ensuring data privacy. By addressing model architecture heterogeneity our approach offers a robust solution for personalized healthcare diagnostics, aligning with the diverse needs of modern healthcare systems and advancing patient-centric AI applications. CFG-SHD enhances personalized diagnosis accuracy, achieving 97.16%, 98.04%, and 97.88% on the PAD-UFES-20, HAM10000, and PH2 datasets, respectively.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| 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