TY - JOUR
T1 - Federated learning for smart cities
T2 - A comprehensive survey
AU - Pandya, Sharnil
AU - Srivastava, Gautam
AU - Jhaveri, Rutvij
AU - Babu, M. Rajasekhara
AU - Bhattacharya, Sweta
AU - Maddikunta, Praveen Kumar Reddy
AU - Mastorakis, Spyridon
AU - Piran, Md Jalil
AU - Gadekallu, Thippa Reddy
PY - 2023/2/1
Y1 - 2023/2/1
N2 - With the advent of new technologies such as the Artificial Intelligence of Things (AIoT), big data, fog computing, and edge computing, smart city applications have suffered from issues, such as leakage of confidential and sensitive information. To envision smart cities, it will be necessary to integrate federated learning (FL) with smart city applications. FL integration with smart city applications can provide privacy preservation and sensitive information protection. In this paper, we present a comprehensive overview of the current and future developments of FL for smart cities. Furthermore, we highlight the societal, industrial, and technological trends driving FL for smart cities. Then, we discuss the concept of FL for smart cities, and numerous FL integrated smart city applications, including smart transportation systems, smart healthcare, smart grid, smart governance, smart disaster management, smart industries, and UAVs for smart city monitoring, as well as alternative solutions and research enhancements for the future. Finally, we outline and analyze various research challenges and prospects for the development of FL for smart cities.
AB - With the advent of new technologies such as the Artificial Intelligence of Things (AIoT), big data, fog computing, and edge computing, smart city applications have suffered from issues, such as leakage of confidential and sensitive information. To envision smart cities, it will be necessary to integrate federated learning (FL) with smart city applications. FL integration with smart city applications can provide privacy preservation and sensitive information protection. In this paper, we present a comprehensive overview of the current and future developments of FL for smart cities. Furthermore, we highlight the societal, industrial, and technological trends driving FL for smart cities. Then, we discuss the concept of FL for smart cities, and numerous FL integrated smart city applications, including smart transportation systems, smart healthcare, smart grid, smart governance, smart disaster management, smart industries, and UAVs for smart city monitoring, as well as alternative solutions and research enhancements for the future. Finally, we outline and analyze various research challenges and prospects for the development of FL for smart cities.
KW - Federated learning
KW - Machine learning
KW - Privacy preservation
KW - Smart city
UR - http://www.scopus.com/inward/record.url?scp=85145265343&partnerID=8YFLogxK
U2 - 10.1016/j.seta.2022.102987
DO - 10.1016/j.seta.2022.102987
M3 - Article
AN - SCOPUS:85145265343
SN - 2213-1388
VL - 55
JO - Sustainable Energy Technologies and Assessments
JF - Sustainable Energy Technologies and Assessments
M1 - 102987
ER -
