Achieving flexible fairness metrics in federated medical imaging

Huijun Xing, Rui Sun, Jinke Ren, Jun Wei, Chun-Mei Feng, Xuan Ding, Zilu Guo, Yu Wang, Yudong Hu, Wei Wei, Xiaohua Ban, Chuanlong Xie (), Yu Tan, Xian Liu, Shuguang Cui, Xiaohui Duan () and Zhen Li ()
Additional contact information
Huijun Xing: The Chinese University of Hong Kong (Shenzhen)
Rui Sun: The Chinese University of Hong Kong (Shenzhen)
Jinke Ren: The Chinese University of Hong Kong (Shenzhen)
Jun Wei: The Chinese University of Hong Kong (Shenzhen)
Chun-Mei Feng: Agency for Science, Technology and Research
Xuan Ding: Beijing Normal University
Zilu Guo: The Chinese University of Hong Kong (Shenzhen)
Yu Wang: Sun Yat-sen University
Yudong Hu: South China Normal University
Wei Wei: Sun Yat-sen University Cancer Center
Xiaohua Ban: Collaborative Innovation Center for Cancer Medicine
Chuanlong Xie: Beijing Normal University
Yu Tan: Guangdong Women and Children Hospital
Xian Liu: The Second Affiliated Hospital of Guangzhou University of Chinese Medicine
Shuguang Cui: The Chinese University of Hong Kong (Shenzhen)
Xiaohui Duan: Sun Yat-sen University
Zhen Li: The Chinese University of Hong Kong (Shenzhen)

Nature Communications, 2025, vol. 16, issue 1, 1-12

Abstract: Abstract The rapid adoption of Artificial Intelligence (AI) in medical imaging raises fairness and privacy concerns across demographic groups, especially in diagnosis and treatment decisions. While federated learning (FL) offers decentralized privacy preservation, current frameworks often prioritize collaboration fairness over group fairness, risking healthcare disparities. Here we present FlexFair, an innovative FL framework designed to address both fairness and privacy challenges. FlexFair incorporates a flexible regularization term to facilitate the integration of multiple fairness criteria, including equal accuracy, demographic parity, and equal opportunity. Evaluated across four clinical applications (polyp segmentation, fundus vascular segmentation, cervical cancer segmentation, and skin disease diagnosis), FlexFair outperforms state-of-the-art methods in both fairness and accuracy. Moreover, we curate a multi-center dataset for cervical cancer segmentation that includes 678 patients from four hospitals. This diverse dataset allows for a more comprehensive analysis of model performance across different population groups, ensuring the findings are applicable to a broader range of patients.

Date: 2025
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DOI: 10.1038/s41467-025-58549-0

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