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Title	Blockchain And Federated Learning Enabled Smart Traffic Management System For Smart Cities
ID_Doc	12359
Authors	Sharma V.; Seetharaman T.; Bd V.; Khangaonkar A.M.
Year	2023
Published	4th International Conference on Intelligent Engineering and Management, ICIEM 2023
DOI	http://dx.doi.org/10.1109/ICIEM59379.2023.10167236
Abstract	In recent years, traffic management across smart cities has become a global concern due to the increasing number of vehicles and data breaches. Addressing and resolving various security challenges associated with smart cities has become a difficult process as the user connects smart traffic infrastructures in a variety of ways such as smartphones, connected devices, etc. Even a slight compromise in real-time traffic data can lead to adverse consequences as they are highly confidential. Blockchain remains to be an efficient technique that can identify and detect security threats and appropriately manage traffic systems. In this paper, we define a blockchain and federated learning-enabled smart traffic management system for smart cities that effectively manages the real-time traffic status and prevent security vulnerabilities in a decentralized manner. The use of the federated learning technique improves the system's efficiency through the use of local and global models. It is observed from the experimental results that the proposed approach remains resistant to byzantine attack and Sybil attacks providing more resilient services to the end users. © 2023 IEEE.
Author Keywords	blockchain; federated learning; smart city; smart traffic management system

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