NeuroCivitas: A Federated Deep Learning Model for Adaptive Urban Intelligence in 6G Cognitive Cities
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Research context
OpenAlex topics: Human Mobility and Location-Based Analysis · Smart Cities and Technologies · Traffic Prediction and Management Techniques
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References (20)
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1. Intelligent traffic flow prediction using optimized GRU model10.1109/ACCESS.2021.3097141 ↗
2. FASTGNN: a topological information protected federated learning approach for traffic speed forecasting10.1109/TII.2021.3055283 ↗
3. Data-driven smart cities enabled by federated learning: challenges and opportunities
4. A joint learning and communications framework for federated learning over wireless networks10.1109/TWC.2020.3024629 ↗
5. Road network traffic flow prediction: a personalized federated learning method based on client reputation10.1016/j.compeleceng.2024.109678 ↗
6. Privacy-preserving AI in 6G urban environments: challenges and solutions
7. Hybrid deep learning model for traffic flow prediction based on attention mechanisms
8. Traffic flow prediction based on federated learning and spatio-temporal graph neural networks10.3390/ijgi13060210 ↗
9. Guan W, Liu J. A summary of traffic flow forecasting methods [Internet]; 2024 [cited 2025 Jun 1]. Available from: https://www.researchgate.net/publication/305223714_A_summary_of_traffic_flow_forecasting_methods.
10. Intelligent 6G networks: wireless connectivity, edge computing, and AI integration
11. Federated learning for network traffic prediction
12. PPSTSL: a privacy-preserving dynamic spatio-temporal graph data federated split learning for traffic forecasting10.1016/j.inffus.2025.103129 ↗
13. Cheikh I, Roy S, Sabir E, Aouami R. Energy, scalability, data and security in massive IoT: current landscape and future directions. arXiv:2505.03036. 2025.
14. On multi-access edge computing: a survey of the emerging 5G network edge architecture and orchestration10.1109/COMST.2017.2705720 ↗
15. Short-term traffic flow prediction based on spatio-temporal analysis and CNN deep learning
16. In-edge AI: intelligentizing mobile edge computing, caching and communication by federated learning10.1109/MNET.2019.1800286 ↗
17. Deep traffic forecasting model for smart city infrastructure
18. Toward federated learning in 6G: solutions, challenges, and future directions
19. Networking systems of AI: on the convergence of computing and communications10.1109/JIOT.2022.3172270 ↗
20. Urban intersection traffic flow prediction: a physics-guided stepwise framework utilizing spatio-temporal graph neural network algorithms10.1016/j.multra.2025.100207 ↗