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.
Bridging the Digital Delivery Gap in Manufacturing
A qualitative guide for manufacturers under pressure to improve margins, lead times and operational reliability.
Based on original interviews with 80+ UK companies, this guide explores the common barriers stopping manufacturers from turning digital investment into measurable delivery improvements.
Building a Digital Culture That Makes Tech Projects Work
A qualitative guide for industry, based on original interviews with 80+ UK companies.
Practical insight for business leaders preparing to invest in digital tools and AI, with a focus on the culture, leadership and adoption behaviours that make technology projects succeed.
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.
Modeling Kidney Transplant Compatibility using a Federated Multi-Layer Perceptron Framework
This study develops a federated multilayer perceptron framework for kidney transplant compatibility prediction.
Leveraging Federated Learning for Predicting Kidney Graft Survival Using OPTN Dataset
The paper presents a federated learning approach for kidney graft survival prediction using the OPTN dataset. The work emphasizes privacy-preserving collaborative learning without sharing patient-level data.
Enhancing Kidney Transplant Compatibility Outcomes through Graph Attention-Based Learning
This research proposes a Graph Attention Network-based framework for modelling donor–recipient relationships. The objective is to improve compatibility assessment by learning complex interactions among transplant variables.
Recognizing Cardiovascular Risk Patterns using Ensemble Learning Techniques
This work investigates cardiovascular risk prediction using ensemble machine learning techniques. It evaluates multiple ensemble models to improve prediction accuracy and support early clinical decision-making.