Federated learning (FL) has been introduced to the healthcare domain as a decentralized learning paradigm that allows multiple parties to train a model collaboratively without privacy leakage. However, most previous studies have assumed that every client holds an identical label set. In reality, medical specialists tend to annotate only diseases within their area of expertise or interest. This implies that label sets in each client can be different and even disjoint. In this paper, we propose the framework FedLSM to solve the problem of Label Set Mismatch. FedLSM adopts different training strategies on data with different uncertainty levels to efficiently utilize unlabeled or partially labeled data as well as class-wise adaptive aggregation in the classification layer to avoid inaccurate aggregation when clients have missing labels. We evaluated FedLSM on two public real-world medical image datasets, including chest X-ray (CXR) diagnosis with 112,120 CXR images and skin lesion diagnosis with 10,015 dermoscopy images, and showed that it significantly outperformed other state-of-the-art FL algorithms. The code can be found at https://github.com/dzp2095/FedLSM.

翻译：联邦学习（FL）作为一种去中心化学习范式已被引入医疗领域，允许多方在无隐私泄露的情况下协同训练模型。然而，以往研究大多假设每个客户端持有完全相同的标签集。现实中，医疗专家通常只标注其专业领域或兴趣范围内的疾病，这意味着各客户端的标签集可能不同甚至互不重叠。本文提出FedLSM框架以解决标签集不匹配问题。该框架对具有不同不确定性的数据采用不同训练策略，有效利用无标签或部分标注数据，并在分类层实施类自适应聚合以避免客户端缺失标签时出现不准确聚合。我们在两个公开真实医疗图像数据集（包含112,120张胸部X光（CXR）影像的胸部X光诊断任务和包含10,015张皮肤镜影像的皮肤病变诊断任务）上进行评估，结果表明FedLSM显著优于其他先进FL算法。代码见https://github.com/dzp2095/FedLSM。