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Article

A Federated Generalized Linear Model for Privacy-Preserving Analysis

Netherlands Comprehensive Cancer Organization (IKNL), 5612 HZ Eindhoven, The Netherlands
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Academic Editor: Laurent Risser
Algorithms 2022, 15(7), 243; https://doi.org/10.3390/a15070243
Received: 14 June 2022 / Revised: 5 July 2022 / Accepted: 11 July 2022 / Published: 13 July 2022
(This article belongs to the Special Issue Interpretability, Accountability and Robustness in Machine Learning)
In the last few years, federated learning (FL) has emerged as a novel alternative for analyzing data spread across different parties without needing to centralize them. In order to increase the adoption of FL, there is a need to develop more algorithms that can be deployed under this novel privacy-preserving paradigm. In this paper, we present our federated generalized linear model (GLM) for horizontally partitioned data. It allows generating models of different families (linear, Poisson, logistic) without disclosing privacy-sensitive individual records. We describe its algorithm (which can be implemented in the user’s platform of choice) and compare the obtained federated models against their centralized counterpart, which were mathematically equivalent. We also validated their execution time with increasing numbers of records and involved parties. We show that our federated GLM is accurate enough to be used for the privacy-preserving analysis of horizontally partitioned data in real-life scenarios. Further development of this type of algorithm has the potential to make FL a much more common practice among researchers. View Full-Text
Keywords: federated learning; Personal Health Train; vantage6 federated learning; Personal Health Train; vantage6
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MDPI and ACS Style

Cellamare, M.; van Gestel, A.J.; Alradhi, H.; Martin, F.; Moncada-Torres, A. A Federated Generalized Linear Model for Privacy-Preserving Analysis. Algorithms 2022, 15, 243. https://doi.org/10.3390/a15070243

AMA Style

Cellamare M, van Gestel AJ, Alradhi H, Martin F, Moncada-Torres A. A Federated Generalized Linear Model for Privacy-Preserving Analysis. Algorithms. 2022; 15(7):243. https://doi.org/10.3390/a15070243

Chicago/Turabian Style

Cellamare, Matteo, Anna J. van Gestel, Hasan Alradhi, Frank Martin, and Arturo Moncada-Torres. 2022. "A Federated Generalized Linear Model for Privacy-Preserving Analysis" Algorithms 15, no. 7: 243. https://doi.org/10.3390/a15070243

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