Variation-Aware Federated Learning with Multi-Source Decentralized Medical Image Data.

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Abstract

Privacy concerns make it infeasible to construct a large medical image dataset by fusing small ones from different sources/institutions. Therefore, federated learning (FL) becomes a promising technique to learn from multi-source decentralized data with privacy preservation. However, the cross-client variation problem in medical image data would be the bottleneck in practice. In this paper, we, for the first time, propose a variation-aware federated learning (VAFL) framework, where the variations among clients are minimized by transforming the images of all clients onto a common image space. We first select one client with the lowest data complexity to define the target image space and synthesize a collection of images based on the client’s raw images. Then, a subset of those synthesized images, which effectively capture the characteristics of the raw images and are sufficiently distinct from any raw image, are carefully selected for sharing. For each client, a modified CycleGAN is applied to translate its raw images to the target image space defined by the shared synthesized images. In this way, the cross-client variation problem is addressed with privacy preservation. We apply the framework for automated classification of clinically significant prostate cancer and evaluate it using multi-source decentralized apparent diffusion coefficient (ADC) image data. Experimental results demonstrate that the proposed VAFL framework stably outperforms the current horizontal FL framework. In addition, we discuss the conditions, and experimentally validated them, that VAFL is applicable for training a global model among multiple clients instead of directly training deep learning models locally on each client. Checking the satisfiability of such conditions can be used as guidance in determining if VAFL or FL should be employed for multi-source decentralized medical image data.