A Systematic Review of Federated Learning in the Healthcare Area: From the Perspective of Data Properties and Applications
Source abstract
Abstract
Recent advances in deep learning have shown many successful stories in smart healthcare applications with data-driven insight into improving clinical institutions’ quality of care. Excellent deep learning models are heavily data-driven. The more data trained, the more robust and more generalizable the performance of the deep learning model. However, pooling the medical data into centralized storage to train a robust deep learning model faces privacy, ownership, and strict regulation challenges. Federated learning resolves the previous challenges with a shared global deep learning model using a central aggregator server. At the same time, patient data remain with the local party, maintaining data anonymity and security. In this study, first, we provide a comprehensive, up-to-date review of research employing federated learning in healthcare applications. Second, we evaluate a set of recent challenges from a data-centric perspective in federated learning, such as data partitioning characteristics, data distributions, data protection mechanisms, and benchmark datasets. Finally, we point out several potential challenges and future research directions in healthcare applications.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Privacy-Preserving Technologies in Data · Artificial Intelligence in Healthcare and Education · Machine Learning in Healthcare
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Science and Information Engineering, Asia University, Taichung City 413, Taiwan
Department of Electrical Engineering, Politeknik Negeri Semarang, Semarang 50275, Indonesia
Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA
Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
Department of Electrical Engineering, Universitas Muhammadiyah Yogyakarta, Bantul 55183, Indonesia
Department of Medical Research, China Medical University Hospital, China Medical University, Taichung City 404, Taiwan
Department of M-Commerce and Multimedia Applications, Asia University, Taichung City 413, Taiwan
Funding
Ministry of Science and Technology
References (77)
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