Meta Clustering for Collaborative Learning

Chenglong Ye, Reza Ghanadan, Jie Ding

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)1160-1169
Number of pages10
JournalJournal of Computational and Graphical Statistics
Volume32
Issue number3
DOIs
StatePublished - 2023

Bibliographical note

Funding Information:
This article is based upon work supported by the National Science Foundation under grant number ECCS-2038603. We thank the anonymous reviewers and Editor for their valuable time and comments, which have helped us improve the original manuscript.

Publisher Copyright:
© 2022 American Statistical Association and Institute of Mathematical Statistics.

Keywords

  • Data integration
  • Distributed computing
  • Fairness
  • Meta clustering
  • Regression

ASJC Scopus subject areas

  • Discrete Mathematics and Combinatorics
  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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