Federated learning (FL), training deep models from decentralized data without privacy leakage, has shown great potential in medical image computing recently. However, considering the ubiquitous class imbalance in medical data, FL can exhibit performance degradation, especially for minority classes (e.g. rare diseases). Existing methods towards this problem mainly focus on training a balanced classifier to eliminate class prior bias among classes, but neglect to explore better representation to facilitate classification performance. In this paper, we present a privacy-preserving FL method named FedIIC to combat class imbalance from two perspectives: feature learning and classifier learning. In feature learning, two levels of contrastive learning are designed to extract better class-specific features with imbalanced data in FL. In classifier learning, per-class margins are dynamically set according to real-time difficulty and class priors, which helps the model learn classes equally. Experimental results on publicly-available datasets demonstrate the superior performance of FedIIC in dealing with both real-world and simulated multi-source medical imaging data under class imbalance. Code is available at https://github.com/wnn2000/FedIIC.

翻译：联邦学习（FL）通过去中心化数据训练深度模型且不泄露隐私，近年来在医学图像计算领域展现出巨大潜力。然而，考虑到医学数据中普遍存在的类别不平衡问题，FL在少数类（如罕见病）上的性能会显著下降。现有应对方法主要聚焦于训练平衡分类器以消除类别先验偏差，但忽略了通过探索更优表征来提升分类性能。本文提出一种名为FedIIC的隐私保护联邦学习方法，从特征学习与分类器学习两个维度应对类别不平衡：在特征学习方面，设计了两层对比学习机制，用于从FL环境下的不平衡数据中提取更优的类别特异性特征；在分类器学习方面，根据实时学习难度与类别先验动态设置各类别边界间隔，促使模型平等学习所有类别。在公开数据集上的实验结果表明，FedIIC在处理真实与模拟的多源医学影像数据类别不平衡问题时均表现出优越性能。代码已开源至https://github.com/wnn2000/FedIIC。