Federated Learning with Homomorphic Encryption: A Privacy-Preserving Solution for Smart Cities

Abstract The rapid proliferation of smart cities has led to an unprecedented generation of sensitive data from interconnected infrastructures, such as healthcare, transportation, energy, and surveillance systems. Ensuring data privacy and security while enabling real-time data analytics remains a cr...

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Hlavní autor:	Ali Alqazzaz
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	Springer 2025-11-01
Edice:	International Journal of Computational Intelligence Systems
Témata:	Federated learning Homomorphic encryption Privacy-preserving Smart cities Data security
On-line přístup:	https://doi.org/10.1007/s44196-025-00829-0
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