LoAdaBoost: Loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care data

By Li Huang, Yifeng Yin, Zeng Fu, Shifa Zhang, Hao Deng, Dianbo Liu. 2018

150 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Health Information Management, Federated & Distributed Learning

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Citations in the corpus

Federated Optimization in Heterogeneous Networks (2018, background): “…on network bandwidth, sending local data to the server (Jeong et al., 2018) violates the key privacy assumption of federated learning, and sending globally-shared proxy data to all devices (Zhao et al., 2018; Huang et al., 2018) requires effort to carefully generate or collect such auxiliary data.”
Two-stage Federated Phenotyping and Patient Representation Learning (2019, methodology): “The strategy of federated learning has been recently adopted in the medical field in structured databased machine learning tasks (Liu et al., 2018a; Huang et al., 2018; Liu et al., 2018b).”
Shifting machine learning for healthcare from development to deployment and from models to data (2022, background): “In healthcare, the focus has been on consensus solutions involving data sharing (a small subset of training data is shared among all institutions 93,94 ).”