Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues

Anichur Rahman; Md Sazzad Hossain; Ghulam Muhammad; Dipanjali Kundu; Tanoy Debnath; Muaz Rahman; Md Saikat Islam Khan; Prayag Tiwari; Shahab S Band

doi:10.1007/s10586-022-03658-4

Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues

Cluster Comput. 2022 Aug 17:1-41. doi: 10.1007/s10586-022-03658-4. Online ahead of print.

Authors

Anichur Rahman^{1

2}, Md Sazzad Hossain², Ghulam Muhammad³, Dipanjali Kundu¹, Tanoy Debnath², Muaz Rahman¹, Md Saikat Islam Khan², Prayag Tiwari⁴, Shahab S Band⁵

Affiliations

¹ Present Address: Department of Computer Science and Engineering, National Institute of Textile Engineering and Research (NITER), Constituent Institute of the University of Dhaka, Savar, Dhaka Bangladesh.
² Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh.
³ Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia.
⁴ Department of Computer Science, Aalto University, Espoo, Finland.
⁵ Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin, 64002 Taiwan.

Abstract

Federated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare field. Traditionally, the healthcare system works based on centralized agents sharing their raw data. Therefore, huge vulnerabilities and challenges are still existing in this system. However, integrating with AI, the system would be multiple agent collaborators who are capable of communicating with their desired host efficiently. Again, FL is another interesting feature, which works decentralized manner; it maintains the communication based on a model in the preferred system without transferring the raw data. The combination of FL, AI, and XAI techniques can be capable of minimizing several limitations and challenges in the healthcare system. This paper presents a complete analysis of FL using AI for smart healthcare applications. Initially, we discuss contemporary concepts of emerging technologies such as FL, AI, XAI, and the healthcare system. We integrate and classify the FL-AI with healthcare technologies in different domains. Further, we address the existing problems, including security, privacy, stability, and reliability in the healthcare field. In addition, we guide the readers to solving strategies of healthcare using FL and AI. Finally, we address extensive research areas as well as future potential prospects regarding FL-based AI research in the healthcare management system.

Keywords: Artificial intelligence; Data management; Explainable AI; Federated learning; IoT; Security; Smart healthcare.