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

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...