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A Systematic Review of Federated Learning in the Healthcare Area: From the Perspective of Data Properties and Applications
| Content Provider | MDPI |
|---|---|
| Author | Prayitno, Shyu, Chi-Ren Putra, Karisma Trinanda Chen, Hsing-Chung Tsai, Yuan-Yu Hossain, K. S. M. Tozammel Jiang, Wei Shae, Zon-Yin |
| Copyright Year | 2021 |
| Description | 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. |
| Starting Page | 11191 |
| e-ISSN | 20763417 |
| DOI | 10.3390/app112311191 |
| Journal | Applied Sciences |
| Issue Number | 23 |
| Volume Number | 11 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-11-25 |
| Access Restriction | Open |
| Subject Keyword | Applied Sciences Information and Library Science Federated Learning Deep Learning Artificial Intelligence Healthcare Data Privacy-preserving |
| Content Type | Text |
| Resource Type | Article |
