  Intensive care data are valuable for improvement of health care, policy
making and many other purposes. Vast amount of such data are stored in
different locations, on many different devices and in different data silos.
Sharing 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, trains 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
possibly different distributions of data from diverse sources. To tackle this
problem, we proposed an adaptive boosting method named LoAdaBoost that
increases the efficiency of federated machine learning. Using intensive care
unit data from hospitals, we investigated the performance of learning in IID
and non-IID data distribution scenarios, and showed that the proposed
LoAdaBoost method achieved higher predictive accuracy with lower computational
complexity than the baseline method.
