Highly efficient homomorphic encryption-based federated learning for diabetic retinopathy classification
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".