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.
Authors
- Luis Alberto Moreno (ORCID: https://orcid.org/0000-0003-0454-653X)
- Iris Pigeot (ORCID: https://orcid.org/0000-0001-7483-0726)
- Timm Intemann (ORCID: https://orcid.org/0000-0001-7836-3643)
- Annika Swenne (ORCID: https://orcid.org/0009-0004-8743-3913)
Institutions
- 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)
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
- Field-Weighted Citation Impact
- 0.00