Federated edge learning for medical image augmentation

In the medical sector, diagnostic technology-related progress is often hindered by data isolation and stringent privacy laws, posing obstacles for institutions that lack extensive disease data. This scarcity impedes the development of precise diagnostic models and reliable auxiliary tools. To address these challenges, we introduce the horizontal federated data augmentation model for medical assistance (HFDAM-MA), a novel approach designed to address the complexities of data scarcity. Our model addresses the limitations of traditional generative adversarial networks (GANs), which often rely on the independent and identically distributed (IID) assumption during training (a condition that is rarely satisfied in real-world medical data scenarios) and face computational challenges in healthcare settings. The HFDAM-MA leverages federated learning (FL) principles to enable non-IID collaborative training across multiple medical institutions. This approach alleviates the data collection pressure at individual sites and ensures the privacy of sensitive medical information. A central node orchestrates the distribution of a unified GAN model to local sites, where it operates in conjunction with two convolutional neural networks (CNNs) to generate synthetic medical images and corresponding labels. Extensive experimental results underscore the effectiveness of our model. As participation increases, we observe a substantial improvement in the diagnostic accuracy of the global model. Moreover, the performance of the local models is bolstered, and the diversity of the generated data is expanded, offering a robust solution to the challenges of data privacy, imbalanced data, and insufficient labeling that are prevalent in the medical sector.