The proliferation of decentralised electronic healthcare records (EHRs) across medical institutions requires innovative federated learning strategies for collaborative data analysis and global model training, prioritising data privacy. A prevalent issue during decentralised model training is the data-view discrepancies across medical institutions that arises from differences or availability of healthcare services, such as blood test panels. The prevailing way to handle this issue is to select a common subset of features across institutions to make data-views consistent. This approach, however, constrains some institutions to shed some critical features that may play a significant role in improving the model performance. This paper introduces a federated learning framework that relies on augmented graph attention networks to address data-view heterogeneity. The proposed framework utilises an alignment augmentation layer over self-attention mechanisms to weigh the importance of neighbouring nodes when updating a node's embedding irrespective of the data-views. Furthermore, our framework adeptly addresses both the temporal nuances and structural intricacies of EHR datasets. This dual capability not only offers deeper insights but also effectively encapsulates EHR graphs' time-evolving nature. Using diverse real-world datasets, we show that the proposed framework significantly outperforms conventional FL methodology for dealing with heterogeneous data-views.