  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.
