A survey of federated learning from data perspective in the healthcare domain : Challenges, methods, and future directions

Taha, Zahraa Khduair and Yaw, Chong Tak and Koh, Siaw Paw and Tiong, Sieh Kiong and Kadirgama, Kumaran and Benedict, Foo and Tan, Jian Ding and Balasubramaniam, Yogendra A.L. (2023) A survey of federated learning from data perspective in the healthcare domain : Challenges, methods, and future directions. IEEE Access, 11. pp. 45711-45735. ISSN 2169-3536. (Published)

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Recent advances in deep learning (DL) have shown that data-driven insights can be used in smart healthcare applications to improve the quality of life for patients. DL needs more data and diversity to build a more accurate system. To satisfy these requirements, more data need to be pooled at the centralized server to train the model deeply, but the process of pooling faces privacy and regulatory challenges. To settle them, the concept of sharing model learning rather than sharing data through federated learning (FL) is proposed. FL creates a more reliable system without transferring data to the server, resulting in the right system with stronger security and access rights to data that protect privacy. This research aims to (1) provide a literature review and an in-depth study on the roles of FL in the fields of healthcare; (2) highlight the effectiveness of current challenges facing standardized FL, including statistical data heterogeneity, privacy and security concerns, expensive communications, limited resources, and efficiency; and (3) present lists of open research challenges and recommendations for future FL for the academic and industrial sectors in telemedicine and remote healthcare applications. An extensive review of the literature on FL from a data-centric perspective was conducted. We searched the Science Direct, IEEE Xplore, and PubMed databases for publications published between January 2018 and January 2023. A new crossover matching between the approaches that solve or mitigate all types of skewed data has been proposed to open up opportunities to other researchers. In addition, a list of various applications was organized by learning application task types such as prediction, diagnosis, and classification. We think that this study can serve as a helpful manual for academics and industry professionals, giving them guidance and important directions for future studies.

Item Type: Article
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Energy; Federated learning; Non-independent identical distribution; Privacy and security
Subjects: T Technology > T Technology (General)
T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TJ Mechanical engineering and machinery
T Technology > TL Motor vehicles. Aeronautics. Astronautics
Faculty/Division: Institute of Postgraduate Studies
College of Engineering
Faculty of Mechanical and Automotive Engineering Technology
Depositing User: Mr Muhamad Firdaus Janih@Jaini
Date Deposited: 04 Sep 2023 06:31
Last Modified: 04 Sep 2023 06:31
URI: http://umpir.ump.edu.my/id/eprint/38111
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