  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.
