Federated Semi-Supervised Learning with Annotation Heterogeneity

Understand

Federated Semi-Supervised Learning (FSSL) aims to learn a global model from different clients in an environment with both labeled and unlabeled data.

• Most of the existing FSSL work generally assumes that both types of data are available on each client.
• In this paper, we study a more general problem setup of FSSL with annotation heterogeneity, where each client can hold an arbitrary percentage (0%-100%) of labeled data.
• To this end, we propose a novel FSSL framework called Heterogeneously Annotated Semi-Supervised LEarning (HASSLE).