Cross-silo federated learning (FL) has attracted much attention in medical imaging analysis with deep learning in recent years as it can resolve the critical issues of insufficient data, data privacy, and training efficiency. However, there can be a generalization gap between the model trained from FL and the one from centralized training. This important issue comes from the non-iid data distribution of the local data in the participating clients and is well-known as client drift. In this work, we propose a novel training framework FedSM to avoid the client drift issue and successfully close the generalization gap compared with the centralized training for medical image segmentation tasks for the first time. We also propose a novel personalized FL objective formulation and a new method SoftPull to solve it in our proposed framework FedSM. We conduct rigorous theoretical analysis to guarantee its convergence for optimizing the non-convex smooth objective function. Real-world medical image segmentation experiments using deep FL validate the motivations and effectiveness of our proposed method.

翻译：近年来，跨站点联邦学习（FL）结合深度学习在医学影像分析领域引起了广泛关注，因为它能够解决数据不足、数据隐私和训练效率等关键问题。然而，通过FL训练的模型与集中式训练的模型之间可能存在泛化差距。这一重要问题源于参与客户端本地数据的非独立同分布（non-iid）特性，即所谓的客户端漂移。本文提出了一种新型训练框架FedSM，用于避免客户端漂移问题，并首次成功消除了与集中式训练相比在医学图像分割任务中的泛化差距。我们还提出了一种新的个性化FL目标函数公式及一种求解方法SoftPull，以应用于所提出的FedSM框架。我们进行了严格的理论分析，以确保其优化非凸平滑目标函数时的收敛性。利用深度FL进行的真实医学图像分割实验验证了我们所提方法的动机和有效性。