Handling Data Heterogeneity with Generative Replay in Collaborative Learning for Medical Imaging

TL;DR

This paper introduces a generative replay approach using adversarial learning to improve collaborative medical imaging models across institutions with heterogeneous data, enhancing accuracy and reducing bias.

Contribution

It proposes a dual model architecture with a generative replay component to effectively handle data heterogeneity in privacy-preserving collaborative learning.

Findings

Achieved ~4.88% accuracy improvement in diabetic retinopathy classification.

Reduced mean absolute error by ~49.8% in Bone Age prediction.

Demonstrated robustness of the method on highly heterogeneous datasets.

Abstract

Collaborative learning, which enables collaborative and decentralized training of deep neural networks at multiple institutions in a privacy-preserving manner, is rapidly emerging as a valuable technique in healthcare applications. However, its distributed nature often leads to significant heterogeneity in data distributions across institutions. In this paper, we present a novel generative replay strategy to address the challenge of data heterogeneity in collaborative learning methods. Different from traditional methods that directly aggregating the model parameters, we leverage generative adversarial learning to aggregate the knowledge from all the local institutions. Specifically, instead of directly training a model for task performance, we develop a novel dual model architecture: a primary model learns the desired task, and an auxiliary "generative replay model" allows aggregating knowledge from the heterogenous clients. The auxiliary model is then broadcasted to the central sever, to regulate the training of primary model with an unbiased target distribution. Experimental results demonstrate the capability of the proposed method in handling heterogeneous data across institutions. On highly heterogeneous data partitions, our model achieves ~4.88% improvement in the prediction accuracy on a diabetic retinopathy classification dataset, and ~49.8% reduction of mean absolution value on a Bone Age prediction dataset, respectively, compared to the state-of-the art collaborative learning methods.