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AuthorElayan, Haya
AuthorAloqaily, Moayad
AuthorGuizani, Mohsen
Available date2022-11-12T12:39:47Z
Publication Date2021-01-01
Publication Name2021 International Wireless Communications and Mobile Computing, IWCMC 2021
Identifierhttp://dx.doi.org/10.1109/IWCMC51323.2021.9498820
CitationElayan, H., Aloqaily, M., & Guizani, M. (2021, June). Deep federated learning for IoT-based decentralized healthcare systems. In 2021 International Wireless Communications and Mobile Computing (IWCMC) (pp. 105-109). IEEE.‏
ISBN9781728186160
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85124783317&origin=inward
URIhttp://hdl.handle.net/10576/36235
AbstractRecent trends in the healthcare industry, such as the use of wearable IoT for continuous health monitoring, are setting new requirements for healthcare systems that boost data analysis. These systems should support decentralization and maintain the privacy and ownership of users' data due to the sensitivity of healthcare data. Therefore, the use of federated learning techniques is recommended for systems that need such requirements. This paper proposes a Deep Federated Learning framework for decentralized healthcare systems that maintain user privacy in a distributed architecture. It also proposes an algorithm for an automated training data acquiring process. Furthermore, it presents an experiment for using deep federated learning in detecting skin diseases and using Transfer Learning to address the problem of limited availability of healthcare data in building deep learning models. The evaluated results show how the federated learning increased the Area Under the Curve of the centralized learning model up to 0.97, as it also shows good model performance during federated rounds in terms of accuracy, precision, recall, and F1-score. Moreover, although the FL system has affected the quality of service to the user in terms of model conversion time, the Federated Learning system meets the requirements of building models in a decentralized manner with no sharing of users' private data.
SponsorThis work was supported by Qatar University under project No: IRCC [2020-003]. The findings achieved herein are solely the responsibility of the authors.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectDeep federated learning
Distributed systems
Healthcare
IoT
Privacy
Transfer learning
TitleDeep Federated Learning for IoT-based Decentralized Healthcare Systems
TypeConference Paper
Pagination105-109
dc.accessType Abstract Only


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