DAG-Based Swarm Learning Approach in Healthcare: A Survey - David Gana; Faisal Jamil (IEEE Access)
Healthcare systems are advancing at a rapid pace as a result of new technologies to address several issues in the sector such as shortage of skilled health workers and to deal with new diseases like COVID-19. Incorporating technologies like blockchain, federated learning, swarm learning, and Directed Acyclic Graphs is transforming healthcare. This article thoroughly examines recent progress and uses at the intersection of these technologies within the healthcare field. Blockchain’s innovative consensus mechanisms and secure data flow systems offer encouraging solutions to crucial issues in healthcare data management and security. Also, federated learning has been deployed in various ways to tackle healthcare challenges enabling collaborative data analysis while upholding patient confidentiality. Swarm learning algorithms have been notably effective in healthcare, enriching medical diagnostics, disease prognosis, and precision medicine. Solutions based on Directed Acyclic Graphs present scalable and effective alternative to traditional blockchain frameworks, providing improved consensus speed and decreased bottlenecks in transaction processing. These advancements signify a shift in direction towards fully decentralised and secure healthcare systems. This paper highlights the transformative impact of these technologies on medical diagnostics, disease prediction, and precision medicine.