Collaborative and privacy-preserving cross-vendor united diagnostic imaging via server-rotating federated machine learning
Abstract Federated Learning (FL) is a distributed framework that enables collaborative training of a server model across medical data vendors while preserving data privacy. However, conventional FL faces two key challenges: substantial data heterogeneity among vendors and limited flexibility from a...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-08-01
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| Series: | Communications Engineering |
| Online Access: | https://doi.org/10.1038/s44172-025-00485-4 |
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