Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration

Federated learning (FL) is revolutionizing healthcare by enabling collaborative machine learning across institutions while preserving patient privacy and meeting regulatory standards. This review delves into FL's applications within smart health systems, particularly its integration with IoT devices, wearables, and remote monitoring, which empower real-time, decentralized data processing for predictive analytics and personalized care. It addresses key challenges, including security risks like adversarial attacks, data poisoning, and model inversion. Additionally, it covers issues related to data heterogeneity, scalability, and system interoperability. Alongside these, the review highlights emerging…

• NR
  National Research Foundation

  Award: BK21 FOUR
• SU
  Sungkyunkwan University

  Award: BK21 FOUR
• NR
  National Research Foundation of Korea
• MO
  Ministry of Education, India
• MO
  Ministry of Science and ICT, South Korea