A survey of federated learning from data perspective in the healthcare domain : Challenges, methods, and future directions
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 cen...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Institute of Electrical and Electronics Engineers Inc.
2023
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| Online Access: | http://umpir.ump.edu.my/id/eprint/38111/ http://umpir.ump.edu.my/id/eprint/38111/1/A%20survey%20of%20federated%20learning%20from%20data%20perspective%20in%20the%20healthcare%20domain.pdf |
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| author | Taha, Zahraa Khduair Yaw, Chong Tak Koh, Siaw Paw Tiong, Sieh Kiong Kadirgama, Kumaran Benedict, Foo Tan, Jian Ding Balasubramaniam, Yogendra A.L. |
| author_facet | Taha, Zahraa Khduair Yaw, Chong Tak Koh, Siaw Paw Tiong, Sieh Kiong Kadirgama, Kumaran Benedict, Foo Tan, Jian Ding Balasubramaniam, Yogendra A.L. |
| author_sort | Taha, Zahraa Khduair |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T03:28:41Z |
| format | Article |
| id | ump-38111 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:28:41Z |
| publishDate | 2023 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-381112023-09-04T06:31:50Z http://umpir.ump.edu.my/id/eprint/38111/ A survey of federated learning from data perspective in the healthcare domain : Challenges, methods, and future directions Taha, Zahraa Khduair Yaw, Chong Tak Koh, Siaw Paw Tiong, Sieh Kiong Kadirgama, Kumaran Benedict, Foo Tan, Jian Ding Balasubramaniam, Yogendra A.L. T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TL Motor vehicles. Aeronautics. Astronautics 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. Institute of Electrical and Electronics Engineers Inc. 2023 Article PeerReviewed pdf en cc_by_nc_nd_4 http://umpir.ump.edu.my/id/eprint/38111/1/A%20survey%20of%20federated%20learning%20from%20data%20perspective%20in%20the%20healthcare%20domain.pdf 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) https://doi.org/10.1109/ACCESS.2023.3267964 https://doi.org/10.1109/ACCESS.2023.3267964 |
| spellingShingle | T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TL Motor vehicles. Aeronautics. Astronautics Taha, Zahraa Khduair Yaw, Chong Tak Koh, Siaw Paw Tiong, Sieh Kiong Kadirgama, Kumaran Benedict, Foo Tan, Jian Ding Balasubramaniam, Yogendra A.L. A survey of federated learning from data perspective in the healthcare domain : Challenges, methods, and future directions |
| title | A survey of federated learning from data perspective in the healthcare domain : Challenges, methods, and future directions |
| title_full | A survey of federated learning from data perspective in the healthcare domain : Challenges, methods, and future directions |
| title_fullStr | A survey of federated learning from data perspective in the healthcare domain : Challenges, methods, and future directions |
| title_full_unstemmed | A survey of federated learning from data perspective in the healthcare domain : Challenges, methods, and future directions |
| title_short | A survey of federated learning from data perspective in the healthcare domain : Challenges, methods, and future directions |
| title_sort | survey of federated learning from data perspective in the healthcare domain : challenges, methods, and future directions |
| topic | T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TL Motor vehicles. Aeronautics. Astronautics |
| url | http://umpir.ump.edu.my/id/eprint/38111/ http://umpir.ump.edu.my/id/eprint/38111/ http://umpir.ump.edu.my/id/eprint/38111/ http://umpir.ump.edu.my/id/eprint/38111/1/A%20survey%20of%20federated%20learning%20from%20data%20perspective%20in%20the%20healthcare%20domain.pdf |