LoAdaBoost:Loss-Based AdaBoost Federated Machine Learning on medical Data

Medical data are valuable for improvement of health care, policy making and many other purposes. Vast amount of medical data are stored in different locations, on many different devices and in different data silos. Sharing medical data among different sources is a big challenge due to regulatory, operational and security reasons. One potential solution is federated machine learning ,which is a method that sends machine learning algorithms simultaneously to all data sources, train models in each source and aggregates the learned models. This strategy allows utilization of valuable data without moving them.One challenge in applying federated machine learning is the heterogeneity of data from different sources. To tackle this problem, we proposed an adaptive boosting method that increases the efficiency of federated machine learning. Using intensive care unit data from hospital, we showed that LoAdaBoost federated learning outperformed baseline method and increased communication efficiency at negligible additional cost.