  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).
Specifically, it is a dual-model framework with two models trained separately
on labeled and unlabeled data such that it can be simply applied to a client
with an arbitrary labeling percentage. Furthermore, a mutual learning strategy
called Supervised-Unsupervised Mutual Alignment (SUMA) is proposed for the dual
models within HASSLE with global residual alignment and model proximity
alignment. Subsequently, the dual models can implicitly learn from both types
of data across different clients, although each dual model is only trained
locally on a single type of data. Experiments verify that the dual models in
HASSLE learned by SUMA can mutually learn from each other, thereby effectively
utilizing the information of both types of data across different clients.
