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Distributed Collaborative Learning with Representative Knowledge Sharing

Author

Listed:
  • Joseph Casey

    (Department of Mathematics, University of Alabama at Birmingham, Birmingham, AL 35294, USA)

  • Qianjiao Chen

    (Department of Mathematics, University of Alabama at Birmingham, Birmingham, AL 35294, USA)

  • Mengchen Fan

    (Department of Computer Science, University of Alabama at Birmingham, Birmingham, AL 35294, USA)

  • Baocheng Geng

    (Department of Computer Science, University of Alabama at Birmingham, Birmingham, AL 35294, USA)

  • Roman Shterenberg

    (Department of Mathematics, University of Alabama at Birmingham, Birmingham, AL 35294, USA)

  • Zhong Chen

    (School of Computing, Southern Illinois University, Carbondale, IL 62901, USA)

  • Keren Li

    (Department of Mathematics, University of Alabama at Birmingham, Birmingham, AL 35294, USA)

Abstract

Distributed Collaborative Learning (DCL) addresses critical challenges in privacy-aware machine learning by enabling indirect knowledge transfer across nodes with heterogeneous feature distributions. Unlike conventional federated learning approaches, DCL assumes non-IID data and prediction task distributions that span beyond local training data, requiring selective collaboration to achieve generalization. In this work, we propose a novel collaborative transfer learning (CTL) framework that utilizes representative datasets and adaptive distillation weights to facilitate efficient and privacy-preserving collaboration. By leveraging Energy Coefficients to quantify node similarity, CTL dynamically selects optimal collaborators and refines local models through knowledge distillation on shared representative datasets. Simulations demonstrate the efficacy of CTL in improving prediction accuracy across diverse tasks while balancing trade-offs between local and global performance. Furthermore, we explore the impact of data spread and dispersion on collaboration, highlighting the importance of tailored node alignment. This framework provides a scalable foundation for cross-domain generalization in distributed machine learning.

Suggested Citation

  • Joseph Casey & Qianjiao Chen & Mengchen Fan & Baocheng Geng & Roman Shterenberg & Zhong Chen & Keren Li, 2025. "Distributed Collaborative Learning with Representative Knowledge Sharing," Mathematics, MDPI, vol. 13(6), pages 1-20, March.
  • Handle: RePEc:gam:jmathe:v:13:y:2025:i:6:p:1004-:d:1615998
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    References listed on IDEAS

    as
    1. Szekely, Gábor J. & Rizzo, Maria L., 2005. "A new test for multivariate normality," Journal of Multivariate Analysis, Elsevier, vol. 93(1), pages 58-80, March.
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