Applications of federated learning in smart cities: recent advances, taxonomy, and open challenges. Issue 1 (31st December 2022)

• Record Type: Journal Article
• Authors: Zheng, Zhaohua; Zhou, Yize; Sun, Yilong; Wang, Zhang; Liu, Boyi; Li, Keqiu
• Is Part Of: Connection science. Volume 34:Issue 1(2022)
• Page Start: 1
• Page End: 28
• Publication Date: 2022-12-31
• Subjects: Federated learning -- smart city -- internet of things; Neural computers -- Periodicals; Artificial intelligence -- Periodicals; Cognitive science -- Periodicals; Connectionism -- Periodicals; 006.3
• DOI: 10.1080/09540091.2021.1936455
• Languages: English
• ISSNs: 0954-0091

Abstract

Abstract : 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.