Advancing Federated Learning Privacy With Quantum Communication Techniques: A Robust Scalable Framework

被引：0

作者：

Pei, Jiaming ^{[1
]}

Wang, Lukun ^{[2
]}

Awan, Nabeela ^{[3
]}

Alturki, Ryan ^{[4
]}

机构：

[1] Univ Sydney, Sch Comp Sci, Sydney, NSW 2006, Australia

[2] Shandong Univ Sci & Technol, Coll Intelligent Equipment, Tai An 271019, Peoples R China

[3] Business Technol Management Grp USA, Aurora, IL 60504 USA

[4] Umm Al Qura Univ, Coll Comp, Dept Software Engn, Mecca 21955, Saudi Arabia

来源：

IEEE SYSTEMS MAN AND CYBERNETICS MAGAZINE | 2025年 / 11卷 / 02期

关键词：

Privacy; Data privacy; Federated learning; Systems architecture; Smart transportation; Servers; Quantum networks; Artificial intelligence; Intelligent transportation systems; Faces;

D O I：

10.1109/MSMC.2024.3509821

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Federated learning (FL) provides a collaborative model allowing multiple entities to collectively train artificial intelligence (AI) systems while minimizing data sharing to enhance privacy. Despite its advancements, traditional FL still faces security issues during the exchange of model parameters. The advent of quantum networks, which offer inherent security benefits, prompts us to introduce a new paradigm: quantum communication-based FL (QC-FL). This robust, scalable framework improves data control and mitigates security risks associated with parameter exchange. Our study outlines the key technologies and architecture underpinning QC-FL, illustrating its utility in safeguarding sensitive patient data and enhancing intelligent transportation systems.

引用

页码：51 / 58

页数：8

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