  Using decentralized data for federated training is one promising emerging
research direction for alleviating data scarcity in the medical domain.
However, in contrast to large-scale fully labeled data commonly seen in general
object recognition tasks, the local medical datasets are more likely to only
have images annotated for a subset of classes of interest due to high
annotation costs. In this paper, we consider a practical yet under-explored
problem, where underrepresented classes only have few labeled instances
available and only exist in a few clients of the federated system. We show that
standard federated learning approaches fail to learn robust multi-label
classifiers with extreme class imbalance and address it by proposing a novel
federated learning framework, FedFew. FedFew consists of three stages, where
the first stage leverages federated self-supervised learning to learn
class-agnostic representations. In the second stage, the decentralized
partially labeled data are exploited to learn an energy-based multi-label
classifier for the common classes. Finally, the underrepresented classes are
detected based on the energy and a prototype-based nearest-neighbor model is
proposed for few-shot matching. We evaluate FedFew on multi-label thoracic
disease classification tasks and demonstrate that it outperforms the federated
baselines by a large margin.
