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Recently, the ever-growing demand for privacy-oriented machine learning has motivated researchers to develop federated and decentralized learning techniques, allowing individual clients to train models collaboratively without disclosing their private datasets.
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M. G. Arivazhagan, V. Aggarwal, A. K. Singh, and S. Choudhary · 2019
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Machine-learning Prognostic Models from the 2014–16 Ebola Outbreak: Data-harmonization Challenges, Validation Strategies, and mHealth Applications
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L. Corinzia, A. Beuret, and J. M. Buhmann · 2019
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T.-M. H. Hsu, H. Qi, and M. Brown · 2019
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Improving federated learning personalization via model agnostic meta learning
Personalized federated learning: A meta-learning approach
A. Fallah, A. Mokhtari, and A. Ozdaglar · 2020
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Pytorch: An imperative style, high-performance deep learning library, 2019
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G. Wang · 2019
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Y. Deng, M. M. Kamani, and M. Mahdavi · 2020
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Predicting Ebola Severity: A Clinical Prioritization Score for Ebola Virus Disease
M. A. Hartley, A. Young, A. M. Tran, H. H. Okoni-Williams, M. Suma, B. Mancuso, A. Al-Dikhari, and M. Faouzi
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A. Imakura, H. Inaba, Y. Okada, and T. Sakurai · 2021
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Ebola Data Platform, 2021
Infectious Disease Data Observatory (IDDO) · 2021
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