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

2021/06/04 by Zhaohua Zheng, Yize Zhou, Yilong Sun +3 · 2 citations
Computer Science · #Age of Information Optimization #Mobile Crowdsensing and Crowdsourcing #Privacy-Preserving Technologies in Data

paper · pdf · doi:10.1080/09540091.2021.1936455

openalex publication_date 2021/06/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29

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.

Citations

• Differentially Private Federated Learning: A Client Level Perspective
• Combining Federated and Active Learning for Communication-efficient Distributed Failure Prediction in Aeronautics
• Federated Learning in Mobile Edge Networks: A Comprehensive Survey
• Asymmetrical Vertical Federated Learning
• Federated Learning: Challenges, Methods, and Future Directions
• Dopamine: Differentially Private Federated Learning on Medical Data

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