Robust data privacy regulations hinder the exchange of healthcare data among institutions, crucial for global insights and developing generalised clinical models. Federated learning (FL) is ideal for training global models using datasets from different institutions without compromising privacy. However, disparities in electronic healthcare records (EHRs) lead to inconsistencies in ML-ready data views, making FL challenging without extensive preprocessing and information loss. These differences arise from variations in services, care standards, and record-keeping practices. This paper addresses data view heterogeneity by introducing a knowledge abstraction and filtering-based FL framework that allows FL over heterogeneous data views without manual alignment or information loss. The knowledge abstraction and filtering mechanism maps raw input representations to a unified, semantically rich shared space for effective global model training. Experiments on three healthcare datasets demonstrate the framework’s effectiveness in overcoming data view heterogeneity and facilitating information sharing in a federated setup.

强大的数据隐私法规阻碍了机构之间的医疗保健数据交换，这对于全球洞察和开发通用临床模型至关重要。联邦学习 （FL） 非常适合使用来自不同机构的数据集训练全局模型，而不会损害隐私。然而，电子医疗记录 （EHR） 的差异导致 ML 就绪数据视图不一致，这使得 FL 具有挑战性，而不会进行大量的预处理和信息丢失。这些差异源于服务、护理标准和记录保存实践的变化。本文通过引入基于知识抽象和过滤的 FL 框架来解决数据视图异构性问题，该框架允许在异构数据视图上进行 FL，而无需手动对齐或丢失信息。知识抽象和过滤机制将原始输入表示映射到一个统一的、语义丰富的共享空间，以实现有效的全局模型训练。在三个医疗保健数据集上的实验证明了该框架在克服数据视图异质性和促进联合设置中信息共享方面的有效性。