  Cross-silo federated learning (FL) enables decentralized organizations to
collaboratively train models while preserving data privacy and has made
significant progress in medical image classification. One common assumption is
task homogeneity where each client has access to all classes during training.
However, in clinical practice, given a multi-label classification task,
constrained by the level of medical knowledge and the prevalence of diseases,
each institution may diagnose only partial categories, resulting in task
heterogeneity. How to pursue effective multi-label medical image classification
under task heterogeneity is under-explored. In this paper, we first formulate
such a realistic label missing setting in the multi-label FL domain and propose
a two-stage method FedMLP to combat class missing from two aspects: pseudo
label tagging and global knowledge learning. The former utilizes a warmed-up
model to generate class prototypes and select samples with high confidence to
supplement missing labels, while the latter uses a global model as a teacher
for consistency regularization to prevent forgetting missing class knowledge.
Experiments on two publicly-available medical datasets validate the superiority
of FedMLP against the state-of-the-art both federated semi-supervised and noisy
label learning approaches under task heterogeneity. Code is available at
https://github.com/szbonaldo/FedMLP.
