(untitled) (Journal of Advanced Artificial Intelligence Research, 2026)
The integration of hyperspectral and LiDAR data has proven essential for high-resolution environmental monitoring, urban planning, and precision agriculture. However, real-world deployments increasingly involve multiple platforms—satellites, UAVs, and ground sensors—each governed by distinct institutional and privacy constraints. Traditional centralized fusion approaches require raw data to be transmitted to a single server, raising significant privacy, regulatory, and bandwidth concerns. This paper introduces Federated HyperFusion, a system-level framework that enables collaborative cross-platform learning for joint hyperspectral and LiDAR fusion without exposing sensitive raw data. We design a federated architecture where distributed clients train local models on their respective data and share only encrypted gradient updates with a central aggregation server. The framework incorporates secure aggregation mechanisms, differential privacy noise calibration, and communication-efficient compression strategies to balance accuracy, privacy, and operational cost. We analyze the structural trade-offs introduced by heterogeneous sensor resolutions, varying label availability, and non-IID data distributions across platforms. The paper further examines governance considerations such as auditability, model accountability, and data sovereignty in multi-stakeholder environments. Infrastructure aspects including edge deployment, real-time inference constraints, and energy sustainability are discussed. We also address fairness and robustness challenges arising from domain shift and biased sensor coverage. Policy implications for cross-jurisdictional data sharing and compliance with emerging privacy regulations are explored. Through this system-level perspective, Federated HyperFusion offers a viable path toward privacy-preserving, collaborative remote sensing analytics that respects institutional boundaries while achieving high-quality fused representations.