  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.
