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Towards Utilizing Unlabeled Data in Federated Learning: A Survey and Prospective - 2020

Research Area:  Machine Learning

Abstract:

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.

Author(s) Name:  Yilun Jin, Xiguang Wei, Yang Liu, Qiang Yang

Journal name:  Machine Learning

Conferrence name:  

Publisher name:  arxiv

DOI:  2002.11545

Volume Information:  Volume 2020