Abstract: In this work, we propose an image segmentation method based on federated style transfer to solve the non-independent and identically distributed (non-IID) problem in federated learning. By sharing style information that is not sensitive to user privacy, this method generates synthetic data for data expansion and reduces data differences between different users while ensuring that important structural information of data is not disclosed. Experiment results show that this method effectively alleviates the influence of non-IID problem among nodes on the performance of the federated model in the liver image segmentation task. Therefore, the proposed method can further improve the performance of federal model, which provides the possibility to break the data island and establish a general model in medical field.

Key words: image segmentation, deep learning, style transfer, federated learning