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 knowledge domain 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 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 evaluate 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 show that it significantly outperforms other state-of-the-art FL algorithms. Code will be made available upon acceptance.

翻译：联邦学习（FL）已被引入医疗领域，作为一种去中心化学习范式，允许多方在不泄露隐私的情况下协作训练模型。然而，以往大多数研究假设每个客户端拥有完全相同的标签集。实际上，医学专家通常仅标注其知识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相交。本文提出了FedLSM框架以解决标签集不匹配问题。FedLSM针对不同不确定水平的数据采用差异化训练策略，从而高效利用无标签或部分标签数据，并在分类层采用类别自适应聚合机制，避免因客户端标签缺失导致不准确聚合。我们在两个公开真实医学图像数据集上评估了FedLSM，包括含112,120张胸部X光片的胸部X光（CXR）诊断数据集和含10,015张皮肤镜图像的皮肤病变诊断数据集，结果表明其显著优于其他最先进的FL算法。代码将在论文接收后公开。