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Distributed Generalized Linear Models: A Privacy-Preserving Approach

Author

Listed:
  • Daniel Tinoco

    (Universidade do Porto
    Universidade do Minho)

  • Raquel Menezes

    (Universidade do Minho)

  • Carlos Baquero

    (Universidade do Porto)

Abstract

This paper presents a novel approach to classical linear regression, enabling accurate model computation from data streams or in a distributed setting while preserving data privacy in federated environments. We extend this framework to generalized linear models (GLMs), ensuring scalability and adaptability to diverse data distributions while maintaining privacy-preserving properties. To assess the effectiveness of our approach, we conduct numerical studies on both simulated and real datasets, comparing our method with conventional maximum likelihood estimation for GLMs using iteratively reweighted least squares. Our results demonstrate the advantages of the proposed method in distributed and federated settings.

Suggested Citation

  • Daniel Tinoco & Raquel Menezes & Carlos Baquero, 2025. "Distributed Generalized Linear Models: A Privacy-Preserving Approach," Computational Statistics, Springer, vol. 40(9), pages 5769-5790, December.
    • Handle: RePEc:spr:compst:v:40:y:2025:i:9:d:10.1007_s00180-025-01673-8
    DOI: 10.1007/s00180-025-01673-8
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    References listed on IDEAS

    as
    1. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521785167.
    2. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521780506.
    3. Inyoung Kim & Noah D. Cohen & Raymond J. Carroll, 2003. "Semiparametric Regression Splines in Matched Case-Control Studies," Biometrics, The International Biometric Society, vol. 59(4), pages 1158-1169, December.
    Full references (including those not matched with items on IDEAS)

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