Federated World Model Learning for Privacy-Preserving Multi-Robot Cooperative Intelligence
Keywords:
federated learning; world models; multi-robot systems; privacy-preserving AI; cooperative intelligence; model-based reinforcement learning; distributed systemsAbstract
The rapid evolution of multi-robot systems toward collaborative autonomy demands learning architectures capable of synthesizing shared environmental understanding without compromising the data sovereignty of individual agents. Federated world model learning emerges as a transformative paradigm that integrates model-based reinforcement learning with privacy-preserving distributed computation, enabling robots to cooperatively construct and maintain predictive models of their operating environments while keeping raw sensory data localized. This paper presents a system-level examination of federated world model learning for multi-robot cooperative intelligence, emphasizing architectural design, governance mechanisms, and the intricate trade-offs that shape deployment viability. We analyze how decentralized latent dynamics models, shared across agents via secure aggregation and differential privacy techniques, can yield collective perception, planning, and adaptation capabilities that surpass those of isolated learners. The discussion develops a layered framework that spans local model design, inter-agent communication protocols, privacy budgets, and global oversight. We interrogate structural tensions between model fidelity and communication efficiency, between fairness in federated participation and statistical heterogeneity, and between robustness to adversarial perturbations and the sustainability of long-running learning processes. The analysis further extends to infrastructure requirements, edge-cloud orchestration, and the regulatory landscapes that will govern autonomous robot collectives. Throughout, we draw on cross-domain insights from distributed systems, sociotechnical governance, and reinforcement learning to illuminate pathways for responsible and resilient deployment. The paper concludes with an outlook on policy levers, standardization needs, and the ethical imperatives that must accompany the maturation of federated world model ecosystems.
References
1. McMahan, B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. y. (2017). Communication-efficient learning of deep networks from decentralized data. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS).
2. Ha, D., & Schmidhuber, J. (2018). World models. arXiv preprint arXiv:1803.10122.
3. Yan, Z., Jouandeau, N., & Cherif, A. A. (2013). A survey and analysis of multi-robot coordination. International Journal of Advanced Robotic Systems, 10(12), 399.
4. Shokri, R., & Shmatikov, V. (2015). Privacy-preserving deep learning. Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security.
5. Dwork, C., McSherry, F., Nissim, K., & Smith, A. (2006). Calibrating noise to sensitivity in private data analysis. Proceedings of the Third Theory of Cryptography Conference (TCC).
6. Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., ... & Thakkar, A. (2017). Practical secure aggregation for privacy-preserving machine learning. Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security.
7. Zhuo, H. H., Feng, W., Xu, Q., Yang, Q., & Liu, Y. (2019). Federated reinforcement learning. arXiv preprint arXiv:1901.08277.
8. Hafner, D., Lillicrap, T., Norouzi, M., & Ba, J. (2020). Dream to control: Learning behaviors by latent imagination. International Conference on Learning Representations (ICLR).
9. Wu, P., Escontrela, A., Hafner, D., Abbeel, P., & Goldberg, K. (2023). DayDreamer: World models for physical robot learning. Proceedings of the 6th Conference on Robot Learning.
10. Wang, H., Liu, Z., & Zhu, Z. (2020). Model-based multi-agent reinforcement learning with cooperative prioritized sweeping. Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS).
11. Sengupta, S., Ganguli, S., & Dey, S. (2020). Privacy preserving multi-agent reinforcement learning for cooperation in antagonistic environments. arXiv preprint arXiv:2011.04821.
12. Li, T., Sanjabi, M., & Smith, V. (2020). Fair resource allocation in federated learning. International Conference on Learning Representations (ICLR).
13. Kehoe, B., Patil, S., Abbeel, P., & Goldberg, K. (2015). A survey of research on cloud robotics and automation. IEEE Transactions on Automation Science and Engineering, 12(2), 398-409.
14. Ghosh, A., Chung, J., Yin, D., & Ramchandran, K. (2020). Robust federated learning in a heterogeneous environment. Advances in Neural Information Processing Systems (NeurIPS).
15. Konečný, J., McMahan, H. B., Yu, F. X., Richtárik, P., Suresh, A. T., & Bacon, D. (2016). Federated learning: Strategies for improving communication efficiency. NIPS Workshop on Private Multi-Party Machine Learning.
16. Hafner, D., Lillicrap, T., Fischer, I., Villegas, R., Ha, D., Lee, H., & Davidson, J. (2019). Learning latent dynamics for planning from pixels. International Conference on Machine Learning (ICML).
17. Bostrom, N., & Yudkowsky, E. (2014). The ethics of artificial intelligence. In K. Frankish & W. M. Ramsey (Eds.), The Cambridge handbook of artificial intelligence (pp. 316-334). Cambridge University Press.
18. Xiong, Z., Song, Y., Kang, H., Yan, Q., Jiang, L., Yang, J., ... & Jacobs, N. (2026). ActWorld: From Explorable to Interactive World Model via Action-Aware Memory. arXiv preprint arXiv:2606.17730.
19. Schwartz, R., Dodge, J., Smith, N. A., & Etzioni, O. (2020). Green AI. Communications of the ACM, 63(12), 54-63.
20. Lim, W. Y. B., Luong, N. C., Hoang, D. T., Jiao, Y., Liang, Y. C., Yang, Q., ... & Miao, C. (2020). Federated learning in mobile edge networks: A comprehensive survey. IEEE Communications Surveys & Tutorials, 22(3), 2031-2063.
21. Li, Q., Wen, Z., Wu, Z., Hu, S., Wang, N., Li, Y., ... & He, B. (2020). A survey on federated learning systems: Vision, hype and reality for data privacy and protection. IEEE Transactions on Knowledge and Data Engineering.
22. Parker, L. E. (2007). ALLIANCE: An architecture for fault tolerant multirobot cooperation. IEEE Transactions on Robotics and Automation, 14(2), 220-240.
23. Li, S., Wu, Y., Cui, X., Dong, H., Fang, F., & Russell, S. (2019). Robust multi-agent reinforcement learning via minimax deep deterministic policy gradient. Proceedings of the AAAI Conference on Artificial Intelligence.
24. Chen, L., Wu, P., Li, X., Wang, Z., & Lin, X. (2024). World models for autonomous driving: A comprehensive survey. arXiv preprint arXiv:2403.14727.
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