Exploring the Possibility of TypiClust for Low-Budget Federated Active Learning
This work addresses annotation efficiency for federated learning applications, but it is incremental as it applies an existing method to a new setting.
The paper tackled the problem of reducing annotation burden in low-budget Federated Active Learning (FAL) by evaluating TypiClust, an existing Active Learning strategy, showing it performs well compared to other methods despite challenges like data heterogeneity and distribution shifts.
Federated Active Learning (FAL) seeks to reduce the burden of annotation under the realistic constraints of federated learning by leveraging Active Learning (AL). As FAL settings make it more expensive to obtain ground truth labels, FAL 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 FAL settings. Our empirical results show that TypiClust works well even in low-budget FAL 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 FAL 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 FAL even in limited data situations.