  Federated Learning (FL) proposed in recent years has received significant
attention from researchers in that it can bring separate data sources together
and build machine learning models in a collaborative but private manner. Yet,
in most applications of FL, such as keyboard prediction, labeling data requires
virtually no additional efforts, which is not generally the case. In reality,
acquiring large-scale labeled datasets can be extremely costly, which motivates
research works that exploit unlabeled data to help build machine learning
models. However, to the best of our knowledge, few existing works aim to
utilize unlabeled data to enhance federated learning, which leaves a
potentially promising research topic. In this paper, we identify the need to
exploit unlabeled data in FL, and survey possible research fields that can
contribute to the goal.
