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.

翻译：在合作学习中,学习者协调他们的每一项学习成绩。从任何学习者的角度来看,一个关键的挑战就是筛选出不合格的合作者。我们提出了一个称为元集的框架来应对这一挑战。与传统的数据集点问题不同,元集群对学习者进行分类。假设每个学习者对独立的本地数据集进行监督回归,我们建议采用选择交换中心(SEC)方法,按其基本的监督功能对学习者进行分类。我们理论上表明,SEC可以将学习者分组为精确的合作组合。经验性研究证实了理论分析,并表明SEC可以具有计算效率,对学习者的异质性具有很强的强度,并且能够有效地提高单项学习者的业绩。此外,我们展示了如何利用拟议的方法提高数据公平性。这一文章的补充材料可在网上查阅。