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Title	A Novel & Innovative Blockchain-Empowered Federated Learning Approach For Secure Data Sharing In Smart City Applications
ID_Doc	3195
Authors	Hai T.; Wang D.; Seetharaman T.; Amelesh M.; Sreejith P.M.; Sharma V.; Ibeke E.; Liu H.
Year	2023
Published	Lecture Notes in Networks and Systems, 735 LNNS
DOI	http://dx.doi.org/10.1007/978-3-031-37164-6_9
Abstract	The very existence of smart cities forms the stepping stone in the evolution of many technological advancements in the future era. While smart cities have already grown in their way, the tremendous amount of data generated from them paves the way for new perspectives of development. This is because security and privacy remain to be the major constraint across smart city applications. Further, smart city applications such as smart homes, smart transportation, and smart healthcare are generating a huge amount of data every day and it is often complex to collect and manage all the data together at a single location. To address these constraints, this paper presents a novel and innovative blockchain-assisted federated learning approach for secure data sharing in IoT Smart Cities. Here, we implement a federated learning approach, where the process of learning is made in a distributed fashion. The use of blockchain in turn adds more security and resilience to smart city applications. The security analysis proves that the proposed approach offers comparatively better performance and remains more resistant to various security threats and vulnerabilities. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Author Keywords	Blockchain; Federated learning; IOT; Secure data sharing; Smart city

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