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The Internet of Things (IoT) revolution has shown potential to give rise to many medical applications with access to large volumes of healthcare data collected by IoT devices.
Project adam: Building an efficient and scalable deep learning training system
Chilimbi, T., Suzue, Y., Apacible, J., and Kalyanaraman, K · 2014
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The internet of things for health care: a comprehensive survey
Islam, S. R., Kwak, D., Kabir, M. H., Hossain, M., and Kwak, K.-S · 2015
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Federated optimization: Distributed machine learning for on-device intelligence
Konečnỳ, J., McMahan, H. B., Ramage, D., and Richtárik, P · 2016
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Artificial intelligence in healthcare: past, present and future
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., and Wang, Y · 2017
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Communication-efficient learning of deep networks from decentralized data
McMahan, B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A · 2017
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The convergence of sparsified gradient methods
Alistarh, D., Hoefler, T., Johansson, M., Konstantinov, N., Khirirat, S., and Renggli, C · 2018
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Internet-of-things and big data for smarter healthcare: From device to architecture, applications and analytics, 2018
Firouzi, F., Rahmani, A. M., Mankodiya, K., Badaroglu, M., Merrett, G. V., Wong, P., and Farahani, B · 2018
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Sparsified sgd with memory
Stich, S. U., Cordonnier, J.-B., and Jaggi, M · 2018
Cited alongside, same era.
Split learning for health: Distributed deep learning without sharing raw patient data
Vepakomma, P., Gupta, O., Swedish, T., and Raskar, R · 2018
Cited alongside, same era.
Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network
Hannun, A. Y., Rajpurkar, P., Haghpanahi, M., Tison, G. H., Bourn, C., Turakhia, M. P., and Ng, A. Y · 2019
Cited alongside, same era.
Detailed comparison of communication efficiency of split learning and federated learning
Singh, A., Vepakomma, P., Gupta, O., and Raskar, R · 2019
Federated learning for healthcare informatics
Xu, J. and Wang, F · 2019
Later among the works it cites.
Federated machine learning: Concept and applications
Yang, Q., Liu, Y., Chen, T., and Tong, Y · 2019
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https://www.hhs.gov/hipaa/for-professionals/privacy/index.html , 2002
Health insurance portability and accountability act privacy rule · 2020
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https://physionet.org/content/challenge-2017/1.0.0/ , 2017
The physionet computing in cardiology challenge 2017 · 2020
Closest in time.
Federated learning in mobile edge networks: A comprehensive survey
Lim, W. Y. B., Luong, N. C., Hoang, D. T., Jiao, Y., Liang, Y.-C., Yang, Q., Niyato, D., and Miao, C · 2020
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Cited alongside, same era.