The emergence of the Internet of Things (IoT) has revolutionized people’s daily lives, providing superior quality services in cognitive cities, healthcare, and smart buildings. However, smart buildings use heterogeneous networks. The massive number of interconnected IoT devices increases the possibility of IoT attacks, emphasizing the necessity of secure and privacy-preserving solutions. Federated Learning (FL) has recently emerged as a promising Machine Learning (ML) paradigm for IoT networks to address these concerns. In FL, multiple devices collaborate to learn a global model without sharing their raw data. However, FL still faces privacy and security concerns due to the transmission of sensitive data (i.e., model parameters) over insecure communication channels. These concerns can be addressed using Fully Homomorphic Encryption (FHE), a powerful cryptographic technique that enables computations on encrypted data without requiring them to be decrypted first. In this study, we propose a secure FL approach in IoT-enabled smart cities that combines FHE and FL to provide secure data and maintain privacy in distributed environments. We present four different FL-based FHE approaches in which data are encrypted and transmitted over a secure medium. The proposed approaches achieved high accuracy, recall, precision, and F-scores, in addition to providing strong privacy and security safeguards. Furthermore, the proposed approaches effectively reduced communication overhead and latency compared to the baseline approach. These approaches yielded improvements ranging from 80.15% to 89.98% in minimizing communication overhead. Additionally, one of the approaches achieved a remarkable latency reduction of 70.38%. The implementation of these security models is non-trivial, and the code is publicly available at https://github.com/Artifitialleap-MBZUAI/Secure-Federated-Learning-with-Fully-Homomorphic-Encryption-for-IoT-Communications.