Evaluation of TypiClust for Federated Active Learning in Low-Budget Regimes

Abstract

Federated Active Learning (FAL) seeks to reduce the burden of annotation under the realistic constraints of Federated Learning by leveraging Active Learning (AL). Federated active learning settings make it more expensive to obtain ground truth labels, so FAL strategies that work well in low-budget regimes are needed. In this work, we investigate the effectiveness of TypiClust, a successful low-budget AL strategy, in FAL settings. Our empirical results show that TypiClust also works well in FAL settings, although these settings present additional challenges, such as data heterogeneity, compared to AL. In addition, our sensitivity analysis of TypiClust to feature extraction methods suggests a way to perform FAL even in limited data situations.