| Title |
Federated learning for smart cities: A comprehensive survey |
| ID_Doc |
39062 |
| Authors |
Pandya, S; Srivastava, G; Jhaveri, R; Babu, MR; Bhattacharya, S; Maddikunta, PKR; Mastorakis, S; Piran, MJ; Gadekallu, TR |
| Title |
Federated learning for smart cities: A comprehensive survey |
| Year |
2023 |
| Published |
|
| DOI |
10.1016/j.seta.2022.102987 |
| Abstract |
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. |
| Author Keywords |
Smart city; Privacy preservation; Machine learning; Federated learning |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000950567400001 |
| WoS Category |
Green & Sustainable Science & Technology; Energy & Fuels |
| Research Area |
Science & Technology - Other Topics; Energy & Fuels |
| PDF |
https://doi.org/10.1016/j.seta.2022.102987
|