NeuroCivitas: A Federated Deep Learning Model for Adaptive Urban Intelligence in 6G Cognitive Cities

The rise of 6G networks and the exponential growth of smart city infrastructures demand intelligent, real-time traffic forecasting solutions that preserve data privacy. This paper introduces NeuroCivitas, a federated deep learning framework that integrates Convolutional Neural Networks (CNNs) for spatial pattern recognition and Long Short-Term Memory (LSTM) networks for temporal sequence modeling. Designed to meet the adaptive intelligence requirements of cognitive cities, NeuroCivitas leverages Federated Averaging (FedAvg) to ensure privacy-preserving training while significantly reducing communication overhead—by 98.7% compared to centralized models. The model is evaluated using the Kaggle Traffic Prediction Dataset comprising 48,120 hourly records from four urban junctions. It achieves an RMSE of 2.76, MAE of 2.11, and an R² score of 0.91, outperforming baseline models such as ARIMA, Linear Regression, Random Forest, and non-federated CNN-LSTM in both accuracy and scalability. Junction-wise and time-based performance analyses further validate its robustness, particularly during off-peak hours, while highlighting challenges in peak traffic forecasting. Ablation studies confirm the importance of both CNN and LSTM layers and temporal feature engineering in achieving optimal performance. Moreover, NeuroCivitas demonstrates stable convergence within 25 communication rounds and shows strong adaptability to non-IID data distributions. The framework is built with real-world deployment in mind, offering support for edge environments through lightweight architecture and the potential for enhancement with differential privacy and adversarial defense mechanisms. SHAP-based explainability is integrated to improve interpretability for stakeholders. In sum, NeuroCivitas delivers an accurate, scalable, and privacy-preserving traffic forecasting solution, tailored for 6G cognitive cities. Future extensions will incorporate fairness-aware optimization, real-time anomaly adaptation, multi-city validation, and advanced federated GNN comparisons to further enhance deployment readiness and societal impact.