Improved Information-Theoretic Generalization Bounds for Distributed, Federated, and Iterative Learning †

We consider information-theoretic bounds on the expected generalization error for statistical learning problems in a network setting. In this setting, there are K nodes, each with its own independent dataset, and the models from the K nodes have to be aggregated into a final centralized model. We co...

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Detalles Bibliográficos
Autores principales: Barnes, Leighton Pate, Dytso, Alex, Poor, Harold Vincent
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9498125/
https://www.ncbi.nlm.nih.gov/pubmed/36141064
http://dx.doi.org/10.3390/e24091178

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9498125/
https://www.ncbi.nlm.nih.gov/pubmed/36141064
http://dx.doi.org/10.3390/e24091178

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