Federated learning for smart cities: A comprehensive survey

Sharnil Pandya, Gautam Srivastava*, Rutvij Jhaveri, M. Rajasekhara Babu, Sweta Bhattacharya, Praveen Kumar Reddy Maddikunta, Spyridon Mastorakis, Md Jalil Piran, Thippa Reddy Gadekallu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

90 Citations (Scopus)


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.

Original languageEnglish
Article number102987
Number of pages13
JournalSustainable Energy Technologies and Assessments
Early online date31 Dec 2022
Publication statusPublished - 1 Feb 2023
Externally publishedYes

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