Revolutionizing healthcare data analytics with federated learning: A comprehensive survey of applications, systems, and future directions

被引：0

作者：

Madathil, Nisha Thorakkattu ^{[1
]}

Dankar, Fida K. ^{[2
]}

Gergely, Marton ^{[1
]}

Belkacem, Abdelkader Nasreddine ^{[3
]}

Alrabaee, Saed ^{[1
]}

机构：

[1] United Arab Emirates Univ, Dept Informat Syst & Secur, Al Ain, U Arab Emirates

[2] CHEO Res Inst, Ottawa, ON, Canada

[3] United Arab Emirates Univ, Dept Comp & Network Engn, Al Ain, U Arab Emirates

来源：

COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL | 2025年 / 28卷

关键词：

Federated learning; Aggregation algorithms; Data privacy; Data partitioning; Non-identically distributed data; Attacks; DIFFERENTIAL PRIVACY; CHALLENGES; FRAMEWORK; TECHNOLOGIES; DIAGNOSIS; SCHEME; RISK;

D O I：

10.1016/j.csbj.2025.06.009

中图分类号：

Q5 [生物化学]; Q7 [分子生物学];

学科分类号：

071010 ; 081704 ;

摘要：

Federated learning (FL)-a distributed machine learning that offers collaborative training of global models across multiple clients. FL has been considered for the design and development of many FL systems in various domains. Hence, we present a comprehensive survey and analysis of existing FL systems, drawing insights from more than 250 articles published in 2019-2024. Our review elucidates the functioning of FL systems, particularly in comparison with alternative distributed learning approaches. Considering the healthcare domain as an example, we define the building blocks of a typical FL healthcare system, including system architecture, federation scale, data partitioning, open-source frameworks, ML models, and aggregation algorithms. Furthermore, we identify and discuss key challenges associated with the design and implementation of FL systems within the healthcare sector while outlining the directions of future research. In general, through systematic categorization and analysis of existing FL systems, we offer insights to design efficient, accurate, and privacy-preserving healthcare applications using cutting-edge FL techniques.

引用

页码：217 / 238

页数：22

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