Federated Learning for Diabetic Retinopathy Screening Across Regional Hospitals

Yusuf Hidayat, Ratna Wulandari, Agus Setiawan

Jurnal Teknologi dan Inovasi, Vol. 1 No. 2 (2026), pp. 61-69

Abstract

Training accurate retinopathy screening models requires large image collections, but hospitals are often unable to share patient data. This study trains a convolutional classifier with federated averaging across four regional hospitals, so that only model updates leave each site. Each hospital contributed between 2,100 and 5,600 graded fundus images. The federated model reached an area under the ROC curve of 0.92 for referable retinopathy, close to the 0.93 achieved by a model trained on pooled data and well above the 0.84 average of models trained at a single site. Sites with fewer images benefited the most. The results show that federated learning can deliver near-centralised accuracy while keeping retinal images inside each hospital.

Keywords: federated learning; diabetic retinopathy; medical imaging; data privacy; deep learning

1. Introduction

This section introduces the problem of medical image analysis addressed in this study, reviews the main approaches reported in prior work, and states the research objectives.

2. Methods

We describe the data, the proposed approach, the baselines, and the evaluation protocol used to assess the results summarised in the abstract.

3. Results and Discussion

The results are presented and compared with the baselines. Limitations and practical implications for deployment are discussed.

4. Conclusion

The study shows that the proposed approach is effective for the stated objectives and outlines directions for future work.

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