Ensemble of Sequential Learning Models With Distributed Data Centers and Its Applications

Handling massive datasets poses a significant challenge in modern data analysis, particularly within epidemiology and medicine. In this study, we introduce a novel approach using sequential ensemble learning to effectively analyze extensive datasets. Our method prioritizes efficiency from both statistical and computational perspectives, addressing challenges such as data communication and privacy, as discussed in federated learning literature. To demonstrate the efficacy of our approach, we present compelling real-world examples using COVID-19 data alongside simulation studies.