دورية أكاديمية
LoAdaBoost: Loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care data.
| العنوان: | LoAdaBoost: Loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care data. |
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| المؤلفون: | Li Huang, Yifeng Yin, Zeng Fu, Shifa Zhang, Hao Deng, Dianbo Liu |
| المصدر: | PLoS ONE, Vol 15, Iss 4, p e0230706 (2020) |
| بيانات النشر: | Public Library of Science (PLoS), 2020. |
| سنة النشر: | 2020 |
| المجموعة: | LCC:Medicine LCC:Science |
| مصطلحات موضوعية: | Medicine, Science |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1932-6203 |
| العلاقة: | https://doaj.org/toc/1932-6203Test |
| DOI: | 10.1371/journal.pone.0230706 |
| الوصول الحر: | https://doaj.org/article/a5a4c34cd9544534b3125bf7757e7169Test |
| رقم الانضمام: | edsdoj.5a4c34cd9544534b3125bf7757e7169 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 19326203 |
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| DOI: | 10.1371/journal.pone.0230706 |