Federated Graph Learning for Resilient Healthcare Infrastructure Modelling in OPTN Transplant Network
This work models OPTN transplant networks using federated graph learning to improve resilience and knowledge sharing across distributed healthcare infrastructures while preserving data privacy.
FGNN-DT: A Privacy-Preserving Federated Graph Learning and Digital Twin Framework for Breast Cancer Recurrence Prediction
The paper integrates federated graph learning with digital twin technology for breast cancer recurrence prediction. The framework combines privacy preservation, graph neural networks, and digital twins for clinically informed prediction.
Comparison of 7 Generic Federated Learning Frameworks on Kidney Graft Survival with Implementation
This comparative experimental study evaluates seven federated learning frameworks—including Flower, FedML, and PySyft—using the OPTN kidney transplant dataset, comparing performance, scalability, communication efficiency, and privacy.
Survey Paper on Federated Learning in Healthcare
This comprehensive survey reviews federated learning architectures, privacy-preserving techniques, healthcare applications, deployment challenges, and future research directions.