A personalized federated learning approach to enhance joint modeling for heterogeneous medical institutions
The FedGMC framework effectively addresses clinical heterogeneity, enhancing predictive performance and ensuring fairness among participating medical institutions. These improvements increase the willingness of data owners to engage in the collaboration FL initiatives.
Authors
- Bo Liu (ORCID: https://orcid.org/0000-0002-9953-8438)
- Yong Hu (ORCID: https://orcid.org/0000-0002-3837-1750)
- Xiangzhou Zhang
- Kang Liu (ORCID: https://orcid.org/0000-0001-9270-1362)
- Weiqi Chen (ORCID: https://orcid.org/0000-0001-6550-5024)
- Ziyuan Liu (ORCID: https://orcid.org/0009-0002-1367-3004)
- Ye Hong
- Eric WT Ngai
Institutions
- Hong Kong Polytechnic University (HK)
- Jinan University (CN)
- Guangdong Polytechnic Normal University (CN)
Publication Details
- Journal
- Digital Health
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1177/20552076251360861
- Citations
- 3
- Primary Topic
- Privacy-Preserving Technologies in Data
- Type
- article
- Field-Weighted Citation Impact
- 7.44
Funders
- National Natural Science Foundation of China