Distributed Generalized Linear Models: A Privacy-Preserving Approach
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866913170200723456 |
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| author | Tinoco, Daniel Menezes, Raquel Baquero, Carlos |
| author_facet | Tinoco, Daniel Menezes, Raquel Baquero, Carlos |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_15287 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Distributed Generalized Linear Models: A Privacy-Preserving Approach Tinoco, Daniel Menezes, Raquel Baquero, Carlos Computation Distributed, Parallel, and Cluster Computing 62J12 (Primary) 68U99 (Secondary) G.3; C.2.4 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. |
| title | Distributed Generalized Linear Models: A Privacy-Preserving Approach |
| topic | Computation Distributed, Parallel, and Cluster Computing 62J12 (Primary) 68U99 (Secondary) G.3; C.2.4 |
| url | https://arxiv.org/abs/2503.15287 |