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  Published Paper Details:

  Paper Title

Privacy-preserving Federated Learning for Smart Cities

  Authors

  Umang H Patel

  Keywords

Privacy-preserving, Federated Learning, Smart Cities

  Abstract


This paper explores the integration of federated learning with smart city frameworks with the goal of developing and deploying privacy-aware systems. The goal is to protect user privacy while utilizing decentralized data for smart city operations including environmental monitoring, public safety, and traffic control. Federated learning is a key technological advancement that allows smart cities to process and analyze data locally, improving service quality and protecting privacy at the same time. In the field of smart city data analytics, this paper demonstrates the revolutionary potential of federated learning, addresses related issues, and provides insight into real-world applications and the technology's future direction.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT24A3304

  Paper ID - 254832

  Page Number(s) - l64-l70

  Pubished in - Volume 12 | Issue 3 | March 2024

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Umang H Patel,   "Privacy-preserving Federated Learning for Smart Cities", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.12, Issue 3, pp.l64-l70, March 2024, Available at :http://www.ijcrt.org/papers/IJCRT24A3304.pdf

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Call For Paper July 2024
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ISSN: 2320-2882
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Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


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ISSN
ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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