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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| Format: | Article |
| Language: | English |
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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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| _version_ | 1849763473565679616 |
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| author | Hao Wang Xiaoyu Zhang Xuebin Ren Zheng Zhang Shusen Yang Chunfeng Lian Jianhua Ma Dong Zeng |
| author_facet | Hao Wang Xiaoyu Zhang Xuebin Ren Zheng Zhang Shusen Yang Chunfeng Lian Jianhua Ma Dong Zeng |
| author_sort | Hao Wang |
| collection | DOAJ |
| description | 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 fixed server, leading to suboptimal performance in diagnostic-imaging tasks. To address these, we propose a server-rotating federated learning method (SRFLM). Unlike traditional FL, SRFLM designates one vendor as a provisional server for federated fine-tuning, with others acting as clients. It uses a rotational server-communication mechanism and a dynamic server-election strategy, allowing each vendor to sequentially assume the server role over time. Additionally, the communication protocol of SRFLM provides strong privacy guarantees using differential privacy. We extensively evaluate SRFLM across multiple cross-vendor diagnostic imaging tasks. We envision SRFLM as paving the way to facilitate collaborative model training across medical data vendors, thereby achieving the goal of cross-vendor united diagnostic imaging. |
| format | Article |
| id | doaj-art-57cc780fac064a7a8b8e107868cffd9a |
| institution | DOAJ |
| issn | 2731-3395 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Engineering |
| spelling | doaj-art-57cc780fac064a7a8b8e107868cffd9a2025-08-20T03:05:24ZengNature PortfolioCommunications Engineering2731-33952025-08-014111610.1038/s44172-025-00485-4Collaborative and privacy-preserving cross-vendor united diagnostic imaging via server-rotating federated machine learningHao Wang0Xiaoyu Zhang1Xuebin Ren2Zheng Zhang3Shusen Yang4Chunfeng Lian5Jianhua Ma6Dong Zeng7School of Biomedical Engineering, Southern Medical UniversitySchool of Biomedical Engineering, Southern Medical UniversityNational Engineering Laboratory for Big Data Analytics, Xi’an Jiaotong UniversityNational Engineering Laboratory for Big Data Analytics, Xi’an Jiaotong UniversityNational Engineering Laboratory for Big Data Analytics, Xi’an Jiaotong UniversityMinistry of Education Key Laboratory for Intelligent Networks and Network Security, Xi’an Jiaotong UniversitySchool of Biomedical Engineering, Southern Medical UniversitySchool of Biomedical Engineering, Southern Medical UniversityAbstract 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 fixed server, leading to suboptimal performance in diagnostic-imaging tasks. To address these, we propose a server-rotating federated learning method (SRFLM). Unlike traditional FL, SRFLM designates one vendor as a provisional server for federated fine-tuning, with others acting as clients. It uses a rotational server-communication mechanism and a dynamic server-election strategy, allowing each vendor to sequentially assume the server role over time. Additionally, the communication protocol of SRFLM provides strong privacy guarantees using differential privacy. We extensively evaluate SRFLM across multiple cross-vendor diagnostic imaging tasks. We envision SRFLM as paving the way to facilitate collaborative model training across medical data vendors, thereby achieving the goal of cross-vendor united diagnostic imaging.https://doi.org/10.1038/s44172-025-00485-4 |
| spellingShingle | Hao Wang Xiaoyu Zhang Xuebin Ren Zheng Zhang Shusen Yang Chunfeng Lian Jianhua Ma Dong Zeng Collaborative and privacy-preserving cross-vendor united diagnostic imaging via server-rotating federated machine learning Communications Engineering |
| title | Collaborative and privacy-preserving cross-vendor united diagnostic imaging via server-rotating federated machine learning |
| title_full | Collaborative and privacy-preserving cross-vendor united diagnostic imaging via server-rotating federated machine learning |
| title_fullStr | Collaborative and privacy-preserving cross-vendor united diagnostic imaging via server-rotating federated machine learning |
| title_full_unstemmed | Collaborative and privacy-preserving cross-vendor united diagnostic imaging via server-rotating federated machine learning |
| title_short | Collaborative and privacy-preserving cross-vendor united diagnostic imaging via server-rotating federated machine learning |
| title_sort | collaborative and privacy preserving cross vendor united diagnostic imaging via server rotating federated machine learning |
| url | https://doi.org/10.1038/s44172-025-00485-4 |
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