Federated Multi-Generator Spatiotemporal Modeling for Privacy-Preserving Autonomous Navigation Prediction
Keywords:
Federated learning, spatiotemporal modeling, multi-generator prediction, autonomous navigation, privacy preservation, trajectory prediction, decentralized infrastructure, governanceAbstract
The rapid deployment of autonomous vehicles and intelligent navigation systems hinges on the ability to predict future trajectories of dynamic agents with high accuracy while preserving the privacy of individual data contributors. Conventional centralized spatiotemporal models require the aggregation of sensitive localization and behavioral data, raising critical concerns regarding data sovereignty, regulatory compliance, and adversarial vulnerability. This paper proposes a federated multi-generator spatiotemporal modeling paradigm that integrates multiple generative trajectory predictors under a federated learning framework to enable privacy-preserving autonomous navigation prediction. The architecture distributes model training across local nodes, each operating on private data, and aggregates only model parameters or synthetic representations rather than raw trajectories. We examine structural trade-offs between prediction fidelity, communication efficiency, and privacy guarantees across various federation topologies. The multi-generator design leverages ensemble diversity to capture complex spatiotemporal dependencies while also providing robustness against distributional shifts and adversarial attacks. Governance mechanisms such as differential privacy budgets, secure aggregation protocols, and decentralized audit trails are analyzed in the context of real-world deployment constraints. Infrastructure considerations including computational heterogeneity, bandwidth limitations, and latency requirements are discussed alongside sustainability and fairness implications. Through cross-domain comparisons with centralized, single-generator, and non-federated approaches, we highlight the advantages and limitations of the proposed framework. The paper concludes with a forward-looking perspective on policy frameworks, standardization efforts, and ethical guidelines necessary for the responsible adoption of federated multi-generator systems in autonomous navigation.
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