Federated Learning-Based Model for Predicting Mortality: Systematic Review and Meta-Analysis.

Journal: Journal of medical Internet research
Published Date: Jul 21, 2025

Abstract

BACKGROUND: The rise of federated learning (FL) as a novel privacy-preserving technology offers the potential to create models collaboratively in a decentralized manner to address confidentiality issues, particularly regarding data privacy. However, there is a scarcity of clear and comprehensive evidence that compares the performance of FL with that of the established centralized machine learning (CML) in the clinical domain.

Authors

  • Nurfaidah Tahir
    Department of Public Health, College of Public Health, China Medical University, No. 100, Section 1, Jingmao Road, Beitun District, Taichung, 406040, Taiwan, 886 422053366 ext 6117.
  • Chau-Ren Jung
    Department of Public Health, College of Public Health, China Medical University, No. 100, Section 1, Jingmao Road, Beitun District, Taichung, 406040, Taiwan, 886 422053366 ext 6117.
  • Shin-Da Lee
    Department of Physical Therapy, Healthcare Science Program, China Medical University, Taichung, Taiwan.
  • Nur Azizah
    Department of Public Health, College of Public Health, China Medical University, No. 100, Section 1, Jingmao Road, Beitun District, Taichung, 406040, Taiwan, 886 422053366 ext 6117.
  • Wen-Chao Ho
    Department of Public Health, College of Public Health, China Medical University, No. 100, Section 1, Jingmao Road, Beitun District, Taichung, 406040, Taiwan, 886 422053366 ext 6117.
  • Tsai-Chung Li
    Department of Public Health, College of Public Health, China Medical University, Taichung, Taiwan; Department of Healthcare Administration, College of Medical and Health Science, Asia University, Taichung, Taiwan.

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