FEDERATED LEARNING IN HEALTHCARE: MODEL MISCONDUCTS, SECURITY, CHALLENGES, APPLICATIONS, AND FUTURE RESEARCH DIRECTIONS-A SYSTEMATIC REVIEW

作     者：Ali, Md Shahin Tasnim, Lamia Ahsan, Md Manjurul Afrin, Sadia Biswas, Koushik Hossain, Md Maruf Ahmed, Md Mahfuz Islam, Md Khairul Hashan, Ronok Raman, Shivakumar 

作者机构：Department of Biomedical Engineering Islamic University Kushtia7003 Bangladesh Department of Food and Nutrition Government College of Applied Human Science Azimpur Dhaka1205 Bangladesh Department of Industrial and Systems Engineering University of Oklahoma Norman OK73071 United States Department of Computer Science and Engineering IIIT Delhi New Delhi Delhi110020 India Department of Electrical and Electronic Engineering Islamic University Kushtia7003 Bangladesh 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2024年

核心收录：

主　　题：Clinical research 

摘      要：Data privacy has become a major concern in healthcare due to the increasing digitization of medical records and data-driven medical research. Protecting sensitive patient information from breaches and unauthorized access is critical, as such incidents can have severe legal and ethical complications. Federated Learning (FL) addresses this concern by enabling multiple healthcare institutions to collaboratively learn from decentralized data without sharing it. FL’s scope in healthcare covers areas such as disease prediction, treatment customization, and clinical trial research. However, implementing FL poses challenges, including model convergence in non-IID (independent and identically distributed) data environments, communication overhead, and managing multi-institutional collaborations. A systematic review of FL in healthcare is necessary to evaluate how effectively FL can provide privacy while maintaining the integrity and usability of medical data analysis. In this study, we analyze existing literature on FL applications in healthcare. We explore the current state of model security practices, identify prevalent challenges, and discuss practical applications and their implications. Additionally, the review highlights promising future research directions to refine FL implementations, enhance data security protocols, and expand FL’s use to broader healthcare applications, which will benefit future researchers and practitioners. © 2024, CC BY.