# Distributed foundational models for multi-task learning in diabetic retinopathy

> **NIH EY R01** · UNIVERSITY OF NORTH CAROLINA CHARLOTTE · 2026 · $617,244

## Abstract

Abstract: This project aims to establish distributed federated learning (FL) approaches for multi-task training of
foundational machine learning (ML) models for diabetic retinopathy (DR), using multi-modal, real-world optical
coherence tomography (OCT) data (OCT cross-section, OCT angiography (OCTA), and OCT enface). DR is one
of the leading causes of severe vision loss. Early detection, prompt intervention, and reliable assessment of
treatment outcomes are essential to prevent irreversible vision loss from DR. However, there are major
challenges towards developing clinically relevant holistic algorithms that can perform multi-tasks, i.e., multi-class
classification of disease stages (diagnosis), prediction of onset and progression of disease stages (prognosis),
and assessment of treatment outcomes. They require large amounts of well curated and labelled datasets from
a diverse sub-population for robust performance. Moreover, efforts towards large, centralized datasets for ML
research are hindered by significant barriers to data sharing and privacy concerns. In this project, we propose to
develop foundational ML models that allow efficient learning of feature representations from a large corpus of
ophthalmic imaging data for various downstream tasks – breaking the task-specific paradigm of current ML
models. We also establish novel federated ML approaches, where the model training is distributed across
institutions instead of sharing patient data. Our first aim is to establish and validate a domain adaptive FL
framework for DR diagnosis across four independent institutions. We propose a novel ophthalmic adaptive
personalized FL (optho-APFL) technique to tackle domain shift caused by heterogeneous data distribution at
different institutions (due to different sub-population density and OCT devices/imaging protocols). We will
conduct experiments on the FL deployment in a clinical setting and integrate a granular differential privacy (DP)
algorithm into our FL framework

## Key facts

- **NIH application ID:** 11208613
- **Project number:** 1R01EY037828-01
- **Recipient organization:** UNIVERSITY OF NORTH CAROLINA CHARLOTTE
- **Principal Investigator:** Minhaj Nur  Alam
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** EY
- **Fiscal year:** 2026
- **Award amount:** $617,244
- **Award type:** 1
- **Project period:** 2026-05-01T00:00:00 → 2030-03-31T00:00:00

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11208613

## Citation

> US National Institutes of Health, RePORTER application 11208613, Distributed foundational models for multi-task learning in diabetic retinopathy (1R01EY037828-01). Retrieved via AI Analytics 2026-05-16 from https://api.ai-analytics.org/grant/nih/11208613. Licensed CC0.

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