HiSatFL: A Hierarchical Federated Learning Framework for Satellite Networks with Cross-Domain Privacy Adaptation

With the proliferation of LEO satellite constellations and increasing demands for on-orbit intelligence, satellite networks generate massive, heterogeneous, and privacy-sensitive data. Ensuring efficient model collaboration under strict privacy constraints remains a critical challenge. This paper proposes HiSatFL, a cross-domain adaptive and privacy-preserving federated learning framework tailored to the highly dynamic and resource-constrained nature of satellite communication systems. The framework incorporates an orbital-aware hierarchical FL architecture, a multi-level domain adaptation mechanism, and an orbit-enhanced meta-learning strategy to enable rapid adaptation with limited samples. In parallel, privacy is preserved via noise-calibrated feature alignment, differentially private adversarial training, and selective knowledge distillation, guided by a domain-aware dynamic privacy budget allocation scheme. We further establish a unified optimization framework balancing privacy, utility, and adaptability, and derive convergence bounds under dynamic topologies. Experimental results on diverse remote sensing datasets demonstrate that HiSatFL significantly outperforms existing methods in accuracy, adaptability, and communication efficiency, highlighting its practical potential for collaborative on-orbit AI.