Federated Learning (FL) has become a promising distributed machine learning paradigm that allows collaborative model training while maintaining user privacy by keeping sensitive data on local devices. However, existing FL-based next-word prediction systems mainly focus on the model performance and lack robust mechanisms to prevent unauthorized users and compromised devices from joining the training process, raising security and reliability issues. To overcome this limitation, in this paper, a secure homogeneous federated learning framework for next-word prediction is proposed by combining a Long Short-Term Memory (LSTM) model with a dual-factor authentication mechanism. The proposed framework includes a central aggregation server and three homogeneous client devices with the same model architecture and training configurations to ensure stable convergence and consistent learning. The dual-factor authentication mechanism integrates the OTP-based user authentication and device authentication to guarantee that only legitimate users and trusted devices can join in the collaborative training. The main metrics for experimental evaluation were prediction accuracy, convergence speed, and security performance. The proposed framework achieved prediction accuracies of ~100%, ~100%, and ~98% across the three participating clients after 50 training epochs, showing faster convergence and more stable learning than a conventional federated learning baseline. Moreover, the authentication mechanism successfully resists unauthorized access with low computational and communication overhead. The results demonstrate that the proposed framework not only improves the accuracy and security of federated next-word prediction but also enhances the trustworthiness, reliability, and practical deployment of privacy-preserving language prediction systems in distributed edge environments.