aspg-20-2405-1791307680 20261006172800 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group International Journal of Wireless and Ad Hoc Communication IJWAC 2692-4056 2024 8 1 Ahmed Hatip Gaziantep university, Turkey Karla Zayood Online Islamic University, Department Of Science and Information Technology, Doha, Qatar Rabah Scharif Applied Engineering Department, Institute of Applied Technology, UAE The security and privacy of data in an IoT-driven intelligence landscape is a major concern. This research examines the integration of Paillier homomorphic encryption into Federated Learning to enhance security while maintaining individual data privacy in such environments. The interconnectedness of devices in IoT frameworks poses a challenge in maintaining the confidentiality of sensitive information. By using Paillier encryption within Federated Learning, this problem is solved by securing learning parameters while still keeping data private. This approach demonstrates promising improvements without violating privacy through extensive simulations and comparative analyses across different model architectures. The results of this study highlight the potential effectiveness of this method for enhancing security measures in interconnected IoT environments. 2024 01 05 2405 https://creativecommons.org/licenses/by/4.0/ 10.54216/IJWAC.080101 https://americaspg.com/journal/20/article/2405 https://americaspg.com/storage/articles/manuscripts/01KWCJ7VG78MBBASF7AEXVD3X7.pdf