Zhang Feilong, Zhai Deming, Bai Guo, Jiang Junjun, Ye Qixiang, Ji Xiangyang, Liu Xianming
Faculty of Computing, Harbin Institute of Technology, Harbin, China.
Shanghai Ninth People's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Nat Commun. 2025 Mar 23;16(1):2852. doi: 10.1038/s41467-025-58055-3.
The widespread integration of AI algorithms in healthcare has sparked ethical concerns, particularly regarding privacy and fairness. Federated Learning (FL) offers a promising solution to learn from a broad spectrum of patient data without directly accessing individual records, enhancing privacy while facilitating knowledge sharing across distributed data sources. However, healthcare institutions face significant variations in access to crucial computing resources, with resource budgets often linked to demographic and socio-economic factors, exacerbating unfairness in participation. While heterogeneous federated learning methods allow healthcare institutions with varying computational capacities to collaborate, they fail to address the performance gap between resource-limited and resource-rich institutions. As a result, resource-limited institutions may receive suboptimal models, further reinforcing disparities in AI-driven healthcare outcomes. Here, we propose a resource-adaptive framework for collaborative learning that dynamically adjusts to varying computational capacities, ensuring fair participation. Our approach enhances model accuracy, safeguards patient privacy, and promotes equitable access to trustworthy and efficient AI-driven healthcare solutions.
人工智能算法在医疗保健领域的广泛整合引发了伦理问题，尤其是在隐私和公平性方面。联邦学习（FL）提供了一个有前景的解决方案，即无需直接访问个人记录就能从广泛的患者数据中学习，在促进跨分布式数据源的知识共享的同时增强隐私保护。然而，医疗机构在获取关键计算资源方面存在显著差异，资源预算往往与人口统计学和社会经济因素相关联，这加剧了参与的不公平性。虽然异构联邦学习方法允许计算能力不同的医疗机构进行协作，但它们未能解决资源有限和资源丰富的机构之间的性能差距。结果，资源有限的机构可能会得到次优模型，进一步加剧人工智能驱动的医疗结果的差异。在此，我们提出一种用于协作学习的资源自适应框架，该框架可动态适应不同的计算能力，确保公平参与。我们的方法提高了模型准确性，保护了患者隐私，并促进了对可靠且高效的人工智能驱动的医疗保健解决方案的公平获取。
