Towards Utilizing Unlabeled Data in Federated Learning: A Survey and Prospective
It addresses the problem of costly labeled data acquisition in FL for researchers and practitioners, but is incremental as it is a survey proposing future directions rather than presenting new results.
This paper identifies the need to exploit unlabeled data in federated learning (FL) to address the high cost of labeling, and surveys potential research fields to enhance FL using unlabeled data, noting that few existing works tackle this topic.
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.