International Journal of Wireless and Ad Hoc Communication IJWAC 2692-4056 10.54216/IJWAC https://www.americaspg.com/journals/show/2405 2019 2019 Gaziantep university, Turkey Ahmed .. Online Islamic University, Department Of Science and Information Technology, Doha, Qatar Karla .. Applied Engineering Department, Institute of Applied Technology, UAE Rabah Scharif 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 2024 08 14 10.54216/IJWAC.080101 https://www.americaspg.com/articleinfo/20/show/2405