Federated Continual Learning for Privacy-Preserving Pedestrian Trajectory Prediction Across Smart Cities
Keywords:
federated learning, continual learning, pedestrian trajectory prediction, privacy preservation, smart cities, data governance, fairness, infrastructure sustainabilityAbstract
Pedestrian trajectory prediction is a critical component of intelligent transportation systems and smart city infrastructures, enabling safer autonomous navigation, crowd management, and urban planning. However, the centralized collection of trajectory data raises significant privacy concerns, as such data can reveal sensitive personal mobility patterns. Federated learning offers a decentralized approach by training models across multiple edge nodes without transferring raw data to a central server. Simultaneously, pedestrian behavior evolves over time and varies across cities, necessitating continual adaptation to new distributions without catastrophic forgetting. This paper proposes a federated continual learning framework for privacy-preserving pedestrian trajectory prediction that operates across heterogeneous smart city deployments. We examine the architectural trade-offs inherent in combining federated aggregation with continual learning mechanisms, including the management of task boundaries, the mitigation of client drift under non‑independent and identically distributed data, and the communication overhead of transmitting model updates. Privacy guarantees are analyzed through differential privacy and secure aggregation, while fairness considerations emerge from uneven data representation across demographic and geographic regions. Governance structures required for multi‑stakeholder coordination, data sovereignty, and regulatory compliance are discussed, alongside sustainability challenges related to energy consumption and device heterogeneity. Deployment scenarios are illustrated through case studies of pedestrian‑dense urban corridors, public transit hubs, and event spaces. The paper also explores policy implications for data ownership, algorithmic accountability, and cross‑jurisdictional model certification. By synthesizing insights from machine learning, infrastructure engineering, and socio‑technical systems, we provide a comprehensive roadmap for deploying trajectory prediction systems that respect individual privacy while maintaining accuracy and adaptivity over long operational horizons.
References
1. McMahan, B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2017). Communication‑efficient learning of deep networks from decentralized data. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS) (pp. 1273–1282).
2. Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A. A., ... & Hadsell, R. (2017). Overcoming catastrophic forgetting in neural networks. Proceedings of the National Academy of Sciences, 114(13), 3521–3526.
3. Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Fei‑Fei, L., & Savarese, S. (2016). Social LSTM: Human trajectory prediction in crowded spaces. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 961–971).
4. Gupta, A., Johnson, J., Fei‑Fei, L., Savarese, S., & Alahi, A. (2018). Social GAN: Socially acceptable trajectories with adversarial networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 2255–2264).
5. Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020). Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine, 37(3), 50–60.
6. Wang, H., Kaplan, Z., Niu, D., & Li, B. (2020). Federated continual learning. arXiv preprint arXiv:2003.06486.
7. Konečný, J., McMahan, H. B., Yu, F. X., Richtárik, P., Suresh, A. T., & Bacon, D. (2016). Federated learning: Strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492.
8. Lopez‑Paz, D., & Ranzato, M. (2017). Gradient episodic memory for continual learning. In Advances in Neural Information Processing Systems (NeurIPS) (pp. 6467–6476).
9. Zhu, P., Zhao, S., Deng, H., & Han, F. (2025). Attentive radiate graph for pedestrian trajectory prediction in disconnected manifolds. IEEE Transactions on Intelligent Transportation Systems.
10. Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., ... & Seth, K. (2017). Practical secure aggregation for privacy‑preserving machine learning. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (CCS) (pp. 1175–1191).
11. Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016). Deep learning with differential privacy. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (CCS) (pp. 308–318).
12. Dwork, C. (2008). Differential privacy: A survey. In International Colloquium on Automata, Languages, and Programming (ICALP) (pp. 1–12).
13. Hadsell, R., Rao, D., Rusu, A. A., & Pascanu, R. (2020). Embracing change: Continual learning in deep neural networks. Trends in Cognitive Sciences, 24(6), 493–504.
14. Zhang, Y., Yang, Q., & Zhang, L. (2021). Fairness in federated learning: A survey. arXiv preprint arXiv:2108.12245.
15. Shokri, R., & Shmatikov, V. (2015). Privacy‑preserving deep learning. In Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security (CCS) (pp. 1310–1321).
16. Zhou, H., Li, J., Luo, Q., & Shen, Y. (2022). Trajectory prediction for autonomous driving: A survey. IEEE Transactions on Intelligent Vehicles, 7(3), 584–601.
17. Rastogi, S., Singh, A., & Kumar, A. (2020). A survey on pedestrian trajectory prediction. ACM Computing Surveys, 53(3), 1–36.
18. Hardt, M., Recht, B., & Singer, Y. (2016). Gradient descent learns linear dynamical systems. Journal of Machine Learning Research, 17(1), 855–888.
19. Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
20. Finn, C., Abbeel, P., & Levine, S. (2017). Model‑agnostic meta‑learning for fast adaptation of deep networks. In Proceedings of the 34th International Conference on Machine Learning (ICML) (pp. 1126–1135).
21. Parisi, G. I., Kemker, R., Part, J. L., Kanan, C., & Wermter, S. (2019). Continual lifelong learning with neural networks: A review. Neural Networks, 113, 54–71.
22. Singh, A., Singh, P., & Kumar, A. (2020). Federated learning with heterogeneous data: A survey. arXiv preprint arXiv:2006.15359.
23. Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., & Chandra, V. (2018). Federated learning with non‑iid data. arXiv preprint arXiv:1806.00582.
24. Mohri, M., Suresh, A. T., & Reyzin, L. (2019). Agnostic federated learning. In Proceedings of the 36th International Conference on Machine Learning (ICML) (pp. 4615–4625).
25. McMahan, H. B., Ramage, D., & Streeter, M. (2018). Learning to communicate with deep multi-agent reinforcement learning. In Advances in Neural Information Processing Systems (NeurIPS) (pp. 1243–1253).
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