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Federated generative prompt learning with vision foundation models: universal efficient multi-center medical image

Xi Lin^1,2, Yuliang Chen^1,2, Jun Wu^3,4

¹School of Computer Science, Shanghai Jiao Tong University, Shanghai, China.

NPJ Digital Medicine

|June 9, 2026

Summary

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Federated Generative Prompt Learning (Fed-GPL) enhances multi-center medical AI by training a prompt generator for precise diagnosis. This efficient framework works with foundation models, even with limited data.

Area of Science:

Artificial Intelligence in Medicine
Medical Imaging Analysis
Collaborative AI

Background:

Federated AI enables multi-center medical collaboration but faces challenges like communication costs and data heterogeneity.
Foundation models (FMs) show promise for medical AI due to their adaptability and generalization.
Existing methods struggle with efficiency and data limitations in federated medical image analysis.

Purpose of the Study:

To introduce Federated Generative Prompt Learning (Fed-GPL), a universal and efficient framework for multi-center medical image analysis.
To address limitations in federated medical AI, including communication costs, data scarcity, and heterogeneity.
To enable precise patient-specific medical diagnosis through customized prompts.

Main Methods:

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Developed Fed-GPL, a framework that collaboratively trains a prompt generator for patient-specific prompt creation.
Integrated Fed-GPL with various vision foundation models, including Vision Transformer (ViT) and Segment Anything (SAM).
Applied Fed-GPL to diverse medical imaging tasks: diabetic retinopathy, melanoma classification, polyp segmentation, and prostate segmentation.

Main Results:

Fed-GPL significantly outperforms traditional models and full fine-tuning approaches.
Achieved high performance with minimal parameter training (8.26% for classification, 6.55% for segmentation).
Demonstrated rapid convergence within 15 communication rounds and maintained performance with only 5% of training data in low-resource settings.

Conclusions:

Fed-GPL offers a universal, efficient, and adaptable solution for federated medical image analysis.
The framework effectively overcomes challenges in multi-center collaboration, data scarcity, and heterogeneity.
Fed-GPL enables precise diagnosis and robust performance across various medical tasks and resource constraints.