在数据有限的联邦学习中，TypiClust能有效减少标注负担。
Exploring the Possibility of TypiClust for Low-Budget Federated Active Learning
- 采用典型性聚类策略选择最具代表性的样本进行标注。
- 在低预算联邦学习下表现优于其他方法，准确率提升显著。
- 对特征提取方式不敏感，适合资源受限场景。
联邦主动学习（FAL）旨在通过主动学习（AL）缓解联邦学习中注释成本高的问题。由于FAL环境下获取真实标签代价更高，亟需适用于低预算场景的策略。本文研究了在低预算FAL设置下表现优异的典型性聚类（TypiClust）的有效性。实验表明，尽管存在数据异构性等额外挑战，与其它方法相比，TypiClust仍保持较高性能。同时发现，FAL会导致典型性分布偏移，但TypiClust对此不敏感。我们还分析了其对特征提取方法的鲁棒性，结果表明该方法可在数据稀缺条件下有效应用。
原文摘要 · Abstract (English)
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.
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