Highly efficient homomorphic encryption-based federated learning for diabetic retinopathy classification
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
Purpose: Diabetic retinopathy (DR) is the leading cause of blindness among working-age adults globally. Although machine learning (ML) has shown promise for DR diagnosis, ensuring model generalizability requires training on data from diverse populations. Federated learning (FL) offers a potential solution by enabling model training on decentralized datasets. However, privacy concerns persist in FL due to potential privacy breaches, such as gradient inversion attacks, which can be used to reconstruct sensitive training data and may discourage participation from patients. Approach: We developed and tested a computationally efficient FL framework that integrates homomorphic encryption (HE) to safeguard patient privacy using 6457 retinal fundus images from the APTOS-2019 and ODIR-5K datasets. First, features are extracted from distributed fundus images using RETFound, a large pretrained foundation model for retinal analysis. These encrypted features are then used to train a lightweight multiclass logistic regression head (MLRH) model for DR grade classification using FL. Results: on ODIR-5K. Efficiency improvements include a 95.9-fold reduction in computation time and a 63.0-fold reduction in data transfer needs compared with fine-tuning the full RETFound model with FL. In addition, results showed that integrating HE effectively protects patient data against gradient inversion attacks. Conclusions: We advance privacy-preserving, ML-based DR screening technology, supporting the goal of equitable vision care worldwide.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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