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

被引：10

作者：

Abbas, Syed Raza ^{[1
]}

Abbas, Zeeshan ^{[2
,3
]}

Zahir, Arifa ^{[1
]}

Lee, Seung Won ^{[2
,3
,4
,5
]}

机构：

[1] COMSATS Univ Islamabad, Dept Biosci, Islamabad 45550, Pakistan

[2] Sungkyunkwan Univ, Sch Med, Dept Precis Med, Suwon 16419, South Korea

[3] Sungkyunkwan Univ, Dept Artificial Intelligence, Suwon 16419, South Korea

[4] Sungkyunkwan Univ, Dept Metabiohlth, Suwon 16419, South Korea

[5] Sungkyunkwan Univ, Personalized Canc Immunotherapy Res Ctr, Sch Med, Suwon 16419, South Korea

来源：

HEALTHCARE | 2024年 / 12卷 / 24期

基金：

新加坡国家研究基金会;

关键词：

artificial intelligence; Internet of Things; machine learning; deep learning; healthcare; big data; ARCHITECTURE; AI;

D O I：

10.3390/healthcare12242587

中图分类号：

R19 [保健组织与事业（卫生事业管理）];

学科分类号：

摘要：

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 privacy-preserving solutions, such as differential privacy and secure multiparty computation, as critical to overcoming FL's limitations. Successfully addressing these hurdles is essential for enhancing FL's efficiency, accuracy, and broader adoption in healthcare. Ultimately, FL offers transformative potential for secure, data-driven healthcare systems, promising improved patient outcomes, operational efficiency, and data sovereignty across the healthcare ecosystem.

引用

页数：33

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