论文标题
在联合学习中利用未标记的数据：一项调查和潜在的
Towards Utilizing Unlabeled Data in Federated Learning: A Survey and Prospective
论文作者
论文摘要
近年来提出的联邦学习（FL）受到了研究人员的极大关注，因为它可以将单独的数据源汇集在一起，并以协作但私人的方式建立机器学习模型。但是，在诸如键盘预测之类的大多数应用程序中，标签数据几乎不需要其他努力，这通常不是这种情况。实际上，获取大规模标记的数据集可能非常昂贵，这激发了利用未标记数据以帮助构建机器学习模型的研究工作。但是，据我们所知，很少有现有的作品旨在利用未标记的数据来增强联邦学习，这留下了一个潜在的有希望的研究主题。在本文中，我们确定需要利用FL中的未标记数据，并调查可能有助于目标的研究领域。
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.
