Guest Editorial: Privacy-Preserving Federated Machine Learning Solutions for Enhanced Security of Critical Energy Infrastructures

被引：3

作者：

Razzak, Imran ^{[1
]}

Xu, Guandong ^{[2
]}

Khan, Muhammad Khurram ^{[3
]}

机构：

[1] Deakin Univ, Geelong, Vic 3220, Australia

[2] Univ Technol Sydney, Broadway, NSW 2007, Australia

[3] King Saud Univ, Riyadh 11451, Saudi Arabia

来源：

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS | 2022年 / 18卷 / 05期

关键词：

Machine learning; Informatics; Critical infrastructure; Privacy; Medical services; Wireless communication; Computer science; Critical Infrastructure; Security; Federated Learning; Cyber incident;

D O I：

10.1109/TII.2021.3128962

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

引用

页码：3449 / 3451

页数：3

共 7 条

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[3]

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[5] Joint Protection of Energy Security and Information Privacy for Energy Harvesting: An Incentive Federated Learning Approach [J].

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[6] SDN-Assisted Safety Message Dissemination Framework for Vehicular Critical Energy Infrastructure [J].

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[7] Lightweight Privacy and Confidentiality Preserving Anonymous Authentication Scheme for WBANs [J].

Subramani, Jegadeesan ;

Maria, Azees ;

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2022, 18 (05) :3484-3491

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