A personalized federated learning approach to enhance joint modeling for heterogeneous medical institutions
Background Federated Learning (FL) offers a privacy-preserving solution for multi-party data collaboration in smart healthcare. However, the data heterogeneity among hospitals and among patients often results in suboptimal performance for some hospitals when applying a global FL model. Current clust...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2025-07-01
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| Series: | Digital Health |
| Online Access: | https://doi.org/10.1177/20552076251360861 |
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