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Decentralized Federated Learning for Healthcare Networks: A Case Study on Tumor Segmentation
Smart healthcare relies on artificial intelligence (AI) functions for learning and analysis of patient data. Since large and diverse datasets for training of Machine Learning (ML) models can rarely be found in individual medical centers, classical centralized AI requires moving privacy-sensitive dat...
| Autores principales: | , , , , , , |
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| Lenguaje: | eng |
| Publicado: |
2022
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| Materias: | |
| Acceso en línea: | https://dx.doi.org/10.1109/access.2022.3141913 http://cds.cern.ch/record/2842572 |