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...
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2024
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my.uniten.dspace-346732024-10-14T11:21:37Z A Survey of Federated Learning From Data Perspective in the Healthcare Domain: Challenges, Methods, and Future Directions Taha Z.K. Yaw C.T. Koh S.P. Tiong S.K. Kadirgama K. Benedict F. Tan J.D. Balasubramaniam Y.A.L. 57202301078 36560884300 22951210700 15128307800 12761486500 57194591957 38863172300 57189520843 Energy federated learning non-independent identical distribution privacy and security Computer aided diagnosis Computer aided instruction Data privacy Deep learning Health care Industrial research Energy Federated learning Health care application Independent identical distributions Medical services Non-independent identical distribution Privacy Privacy and security Security Internet of things 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. � 2013 IEEE. Final 2024-10-14T03:21:37Z 2024-10-14T03:21:37Z 2023 Article 10.1109/ACCESS.2023.3267964 2-s2.0-85153534096 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153534096&doi=10.1109%2fACCESS.2023.3267964&partnerID=40&md5=65c93989d4a56d78861605d42848a85c https://irepository.uniten.edu.my/handle/123456789/34673 11 45711 45735 All Open Access Gold Open Access Institute of Electrical and Electronics Engineers Inc. Scopus |
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Energy federated learning non-independent identical distribution privacy and security Computer aided diagnosis Computer aided instruction Data privacy Deep learning Health care Industrial research Energy Federated learning Health care application Independent identical distributions Medical services Non-independent identical distribution Privacy Privacy and security Security Internet of things |
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Energy federated learning non-independent identical distribution privacy and security Computer aided diagnosis Computer aided instruction Data privacy Deep learning Health care Industrial research Energy Federated learning Health care application Independent identical distributions Medical services Non-independent identical distribution Privacy Privacy and security Security Internet of things Taha Z.K. Yaw C.T. Koh S.P. Tiong S.K. Kadirgama K. Benedict F. Tan J.D. Balasubramaniam Y.A.L. A Survey of Federated Learning From Data Perspective in the Healthcare Domain: Challenges, Methods, and Future Directions |
| 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 |
| author2 |
57202301078 |
| author_facet |
57202301078 Taha Z.K. Yaw C.T. Koh S.P. Tiong S.K. Kadirgama K. Benedict F. Tan J.D. Balasubramaniam Y.A.L. |
| format |
Article |
| author |
Taha Z.K. Yaw C.T. Koh S.P. Tiong S.K. Kadirgama K. Benedict F. Tan J.D. Balasubramaniam Y.A.L. |
| author_sort |
Taha Z.K. |
| title |
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_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_sort |
survey of federated learning from data perspective in the healthcare domain: challenges, methods, and future directions |
| publisher |
Institute of Electrical and Electronics Engineers Inc. |
| publishDate |
2024 |
| _version_ |
1814061190112870400 |
| score |
13.222552 |