Distributed Generalized Linear Models: A Privacy-Preserving Approach

Daniel Tinoco (), Raquel Menezes () and Carlos Baquero ()
Additional contact information
Daniel Tinoco: Universidade do Porto
Raquel Menezes: Universidade do Minho
Carlos Baquero: Universidade do Porto

Computational Statistics, 2025, vol. 40, issue 9, No 31, 5769-5790

Abstract: 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.

Keywords: Generalized linear models; Distributed statistical computing; Federated learning; Privacy-preserving machine learning; Data stream analysis; Scalable regression model (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00180-025-01673-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:compst:v:40:y:2025:i:9:d:10.1007_s00180-025-01673-8

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/180/PS2

DOI: 10.1007/s00180-025-01673-8

Access Statistics for this article

Computational Statistics is currently edited by Wataru Sakamoto, Ricardo Cao and Jürgen Symanzik

More articles in Computational Statistics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().