Federated generalized additive models for location, scale and shape

In this paper, we propose a privacy-enhancing federated GAMLSS that yields virtually identical results as the original GAMLSS algorithm, without the need to physically pool the data.

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Publication Details

Journal
BMC Medical Research Methodology
Published
2025-12-08
DOI
https://doi.org/10.1186/s12874-025-02735-7
Primary Topic
Privacy-Preserving Technologies in Data
Type
article
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article

Federated generalized additive models for location, scale and shape

Luis Alberto Moreno, Iris Pigeot, Timm Intemann, Annika Swenne
BMC Medical Research Methodology
Privacy-Preserving Technologies in Data
article

Federated generalized additive models for location, scale and shape

Luis Alberto Moreno, Iris Pigeot, Timm Intemann, Annika Swenne
article en

Abstract

In this paper, we propose a privacy-enhancing federated GAMLSS that yields virtually identical results as the original GAMLSS algorithm, without the need to physically pool the data.

BMC Medical Research MethodologyVol. 25(1)
University of Bremen (DE), Universidad de Zaragoza (ES), Instituto de Salud Carlos III (ES), Leibniz Institute for Prevention Research and Epidemiology - BIPS (DE), Instituto de Investigación Sanitaria Aragón (ES), Instituto Aragonés de Ciencias de la Salud (ES)
Openalex Percentile: Top 79%
Privacy-Preserving Technologies in Data
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Federated generalized additive models for location, scale and shape — Luis Alberto Moreno, Iris Pigeot, et al. · BMC Medical Research Methodology (2025) | TGRS Research Map | TGRS