Kim Tae Hyun, Yu Jae Yong, Jang Won Seok, Heo Sun Cheol, Sung MinDong, Hong JaeSeong, Chung KyungSoo, Park Yu Rang
Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea.
Research Institute for Data Science and AI (Artificial Intelligence), Hallym University, Chuncheon-si, Gangwon-do, Republic of Korea.
iScience. 2024 Sep 13;27(10):110943. doi: 10.1016/j.isci.2024.110943. eCollection 2024 Oct 18.
Federated learning (FL) in healthcare allows the collaborative training of models on distributed data sources, while ensuring privacy and leveraging collective knowledge. However, as each institution collects data separately, conventional FL cannot leverage the different features depending on the institution. We proposed a personalized progressive FL (PPFL) approach that leverages client-specific features and evaluated with real-world datasets. We compared the performance of in-hospital mortality prediction between our model and conventional models based on accuracy and area under the receiver operating characteristic (AUROC). PPFL achieved an accuracy of 0.941 and AUROC of 0.948, which were higher than the scores of the local models and FedAvg algorithm. We also observed that PPFL achieved a similar performance for cancer data. We identified client-specific features that can contribute to mortality. PPFL is a personalized federated algorithm for heterogeneously distributed clients that expands the feature space for client-specific vertical feature information.
医疗保健领域的联邦学习（FL）允许在分布式数据源上进行模型的协作训练，同时确保隐私并利用集体知识。然而，由于每个机构分别收集数据，传统的联邦学习无法根据机构利用不同的特征。我们提出了一种个性化渐进式联邦学习（PPFL）方法，该方法利用特定于客户端的特征，并使用真实世界数据集进行评估。我们基于准确率和受试者工作特征曲线下面积（AUROC）比较了我们的模型与传统模型在院内死亡率预测方面的性能。PPFL实现了0.941的准确率和0.948的AUROC，高于本地模型和联邦平均算法的得分。我们还观察到PPFL在癌症数据上取得了类似的性能。我们确定了可能导致死亡的特定于客户端的特征。PPFL是一种针对异构分布式客户端的个性化联邦算法，它扩展了用于特定于客户端的垂直特征信息的特征空间。
