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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...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
MDPI
2022
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| Materias: | |
| 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 |