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Federated learning with hyper-network-a case study on whole slide image analysis.

Yanfei Lin¹, Haiyi Wang¹, Weichen Li¹

¹China Telecom Research Institute, Guangzhou, 510000, China.

Scientific Reports

|January 31, 2023

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Federated learning (FL) enhances AI privacy by decentralizing data training. A novel hyper-network approach improves whole slide image classification accuracy using multi-center data without sharing sensitive information.

Area of Science:

Artificial Intelligence
Medical Image Analysis
Data Privacy

Background:

Developing accurate deep learning models requires diverse, high-quality data.
Data privacy concerns hinder multi-center data sharing in sensitive domains like medical imaging.

Purpose of the Study:

To present a privacy-preserving federated learning framework for whole slide image classification.
To address the challenge of leveraging multi-center data without compromising privacy.

Main Methods:

A federated learning (FL) paradigm using a hyper-network at the global center.
Local clients employ multiple-instance learning for whole slide image classification.
Secure transfer of noisy model parameters instead of raw data.

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Main Results:

The proposed FL model significantly improves whole slide image classification accuracy.
The hyper-network approach effectively leverages multi-center data.
Performance surpasses isolated local centers and federated averaging baselines.

Conclusions:

Federated learning with hyper-networks offers a scalable and accurate solution for privacy-preserving medical image analysis.
This method enables the development of robust AI models using distributed sensitive datasets.
The framework demonstrates significant improvements in whole slide image classification tasks.