Improving Semi-Supervised Federated Learning with Limited Labeled Data via Adaptive Batchsize and Pseudo Labeling

Federated learning (FL) is a distributed learning method that leverages numerous edge devices for training while protecting data privacy. However, most of the data produced by distributed edge devices are unlabeled, leading to the emergence of semi-supervised federated learning (SSFL) to address this issue. Although state-of-the-art approaches perform well when labeled data is sufficient, it shows severe performance degradation and training becomes more challenging as labeled data becomes scarce. In this paper, we improve performance in environments with limited labeled data by dynamically adjusting batch sizes and applying an Adaptive Threshold (AT). Additionally, we propose methods to resolve issues arising when adopting Adaptive Thresholds method of the centralized approach and investigate their limitations.