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

Zheng, Zhaohua ; Zhou, Yize ; Sun, Yilong ; Wang, Zhang ; Liu, Boyi ; Li, Keqiu

Connection science, 2022-12, Vol.34 (1), p.1-28 [Periódico revisado por pares]

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Título:
Applications of federated learning in smart cities: recent advances, taxonomy, and open challenges
Autor: Zheng, Zhaohua ; Zhou, Yize ; Sun, Yilong ; Wang, Zhang ; Liu, Boyi ; Li, Keqiu
Assuntos: Artificial intelligence ; Big Data ; Federated learning ; Internet of Things ; smart city ; Taxonomy
É parte de: Connection science, 2022-12, Vol.34 (1), p.1-28
Descrição: 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.
Editor: Abingdon: Taylor & Francis
Idioma: Inglês

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