Highly efficient homomorphic encryption-based federated learning for diabetic retinopathy classification.

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: Experimental results show that the MLRH model trained using FL achieves similar performance compared with a fully fine-tuned RETFound model on centralized data, with the area under the receiver operating characteristic curve scores of $0.93\pm 0.01$ on APTOS-2019 and $0.78\pm 0.02$ 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.