2 papers
cs.CY2020
Federated machine learning with Anonymous Random Hybridization (FeARH) on medical records
Jianfei Cui, He Zhu, Hao Deng +2
Sometimes electrical medical records are restricted and difficult to centralize for machine learning, which could only be trained in distributed manner that involved many instituti…
cs.LG2018
LoAdaBoost: loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care data
Li Huang, Yifeng Yin, Zeng Fu +3
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 differen…