Distributed Generalized Linear Models: A Privacy-Preserving Approach

Daniel Tinoco; Raquel Menezes; Carlos Baquero

doi:10.1007/s00180-025-01673-8

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arXiv · 2503.15287

Distributed Generalized Linear Models: A Privacy-Preserving Approach

Daniel Tinoco·Raquel Menezes·Carlos Baquero

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Abstract

This paper presents a novel approach to classical linear regression, enabling 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.

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BibTeX RIS

Daniel Tinoco, Raquel Menezes, Carlos Baquero. 2025-03-19. Distributed Generalized Linear Models: A Privacy-Preserving Approach. https://doi.org/10.1007/s00180-025-01673-8

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