The rapid expansion of Internet of Things (IoT) devices and advancements in mobile computing have underscored the significance of edge computing in addressing data processing and analytics challenges at the network. Federated Learning (FL) emerges as a strategic response, enabling collaborative model training across multiple participants without necessitating the sharing of raw data, thus preserving privacy and effectively leveraging decentralized data. However, FL’s architecture presents distinct security challenges, particularly in safeguarding data and models against unauthorized access and manipulation. This paper presents a FL framework that integrates Attribute-Based Encryption (ABE) to provide fine-grained data access control while ensuring data security. In addition, we employ device fingerprints to enhance user authentication and device security, ensuring that only verified devices can participate in the model training process, thereby minimizing the risks of malicious access and data breaches. This paper delineates our contributions towards enhancing FL security, including the deployment of encryption methodologies, access management, and a comprehensive discussion on security mechanisms.