Exploring the Possibility of TypiClust for Low-Budget Federated Active Learning

--Federated Active Learning (F AL) seeks to reduce the burden of annotation under the realistic constraints of federated learning by leveraging Active Learning (AL). As F AL settings make it more expensive to obtain ground truth labels, F AL strategies that work well in low-budget regimes, where the amount of annotation is very limited, are needed. In this work, we investigate the effectiveness of TypiClust, a successful low-budget AL strategy, in low-budget F AL settings. Our empirical results show that TypiClust works well even in low-budget F AL settings contrasted with relatively low performances of other methods, although these settings present additional challenges, such as data heterogeneity, compared to AL. In addition, we show that F AL settings cause distribution shifts in terms of typicality, but TypiClust is not very vulnerable to the shifts. We also analyze the sensitivity of TypiClust to feature extraction methods, and it suggests a way to perform F AL even in limited data situations.