Meta Clustering for Collaborative Learning

TL;DR

This paper introduces a meta clustering framework with the SEC method to classify learners in collaborative learning, improving performance and fairness by filtering unqualified collaborators.

Contribution

The paper proposes a novel SEC method for meta clustering of learners, providing theoretical guarantees and empirical evidence of its effectiveness and efficiency.

Findings

SEC accurately clusters learners into collaboration sets

SEC is robust to learner heterogeneity

Using SEC enhances individual learner performance and data fairness

Abstract

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.