Applications of federated learning in smart cities: recent advances, taxonomy, and open challenges

被引：105
|
作者：
Zheng, Zhaohua [1 ,2 ]
Zhou, Yize [3 ]
Sun, Yilong [4 ]
Wang, Zhang [5 ]
Liu, Boyi [6 ]
Li, Keqiu [7 ]
机构：
[1] Tianjin Univ, Coll Intelligence & Comp, Sch Comp Sci & Technol, Tianjin, Peoples R China
[2] Hainan Univ, Sch Cryptol, Sch CyberSpace, Haikou, Hainan, Peoples R China
[3] Hainan Univ, Sch Sci, Haikou, Hainan, Peoples R China
[4] Hainan Univ, Sch Management, Haikou, Hainan, Peoples R China
[5] Hainan Univ, Sch Informat & Commun Engn, Haikou, Hainan, Peoples R China
[6] Univ Macau, State Key Lab Internet Things Smart City IoTSC, Taipa, Macao, Peoples R China
[7] Tianjin Univ, Coll Intelligence & Comp, Tianjin, Peoples R China
来源：
CONNECTION SCIENCE | 2022年 / 34卷 / 01期
关键词：
Federated learning; smart city; internet of things; HEALTH-CARE; INTERNET; NETWORKS; VEHICLES; COMMUNICATION; BLOCKCHAIN; FRAMEWORK;
D O I：
10.1080/09540091.2021.1936455
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Federated learning (FL) plays an important role in the development of smart cities. With the evolution of big data and artificial intelligence, issues related to data privacy and protection have emerged, which can be solved by FL. In this paper, the current developments in FL and its applications in various fields are reviewed. With a comprehensive investigation, the latest research on the application of FL is discussed for various fields in smart cities. We explain the current developments in FL in fields, such as the Internet of Things (IoT), transportation, communications, finance, and medicine. First, we introduce the background, definition, and key technologies of FL. Then, we review key applications and the latest results. Finally, we discuss the future applications and research directions of FL in smart cities.
引用
收藏
页码：1 / 28
页数：28
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