Performance Enhancement in Federated Learning by Reducing Class Imbalance of Non-IID Data

Due to the distributed data collection and learning in federated learnings, many clients conduct local training with non-independent and identically distributed (non-IID) datasets. Accordingly, the training from these datasets results in severe performance degradation. We propose an efficient algori...

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Detalles Bibliográficos
Autores principales: Seol, Mihye, Kim, Taejoon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9919903/
https://www.ncbi.nlm.nih.gov/pubmed/36772192
http://dx.doi.org/10.3390/s23031152

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9919903/
https://www.ncbi.nlm.nih.gov/pubmed/36772192
http://dx.doi.org/10.3390/s23031152

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