A Survey of Federated Learning From Data Perspective in the Healthcare Domain: Challenges, Methods, and Future Directions

• Main Authors: Taha Z.K., Yaw C.T., Koh S.P., Tiong S.K., Kadirgama K., Benedict F., Tan J.D., Balasubramaniam Y.A.L.
• Other Authors: 57202301078
• Format: Article
• Published: Institute of Electrical and Electronics Engineers Inc. 2024
• Subjects: Energy; federated learning; non-independent identical distribution; privacy and security; Computer aided diagnosis; Computer aided instruction; Data privacy; Deep learning; Health care; Industrial research; Federated learning; Health care application; Independent identical distributions; Medical services; Non-independent identical distribution; Privacy; Privacy and security; Security; Internet of things
• Institution: Universiti Tenaga Nasional

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