论文标题
用于协作学习的元聚类
Meta Clustering for Collaborative Learning
论文作者
论文摘要
在协作学习中，学习者协调以增强他们的每个学习表现。从任何学习者的角度来看，一个关键的挑战是滤除不合格的合作者。我们建议一个名为Meta聚类的框架来应对挑战。与群集数据点的经典问题不同，元聚类将学习者分类。假设每个学习者在独立的本地数据集上执行监督回归，我们建议选择一种选择 - 交换群集（SEC）方法，以通过其基本的监督功能对学习者进行分类。从理论上讲，我们可以表明SEC可以将学习者聚集到准确的协作集中。实证研究证实了理论分析，并证明SEC可以在计算上是有效的，对学习者异质性的稳健性，并且有效地增强了单人学习者的性能。另外，我们展示了如何使用所提出的方法来增强数据公平性。本文的补充材料可在线获得。
In collaborative learning, learners coordinate to enhance each of their learning performances. From the perspective of any learner, a critical challenge is to filter out unqualified collaborators. We propose a framework named meta clustering to address the challenge. Unlike the classical problem of clustering data points, meta clustering categorizes learners. Assuming each learner performs a supervised regression on a standalone local dataset, we propose a Select-Exchange-Cluster (SEC) method to classify the learners by their underlying supervised functions. We theoretically show that the SEC can cluster learners into accurate collaboration sets. Empirical studies corroborate the theoretical analysis and demonstrate that SEC can be computationally efficient, robust against learner heterogeneity, and effective in enhancing single-learner performance. Also, we show how the proposed approach may be used to enhance data fairness. Supplementary materials for this article are available online.
