Space-air-ground integrated networks (SAGINs) interconnect satellites, uncrewed aerial vehicles (UAVs), and ground devices to enable flexible and ubiquitous wireless services. The integration of reconfigurable intelligent surfaces (RISs) and fluid antenna systems (FASs) further enhances radio environment controllability. However, the tight integration of cross-layer facilities and radio enhancement technologies leads to pronounced environmental dynamics and heterogeneity, posing fundamental challenges for system modeling and optimization in large-scale SAGINs. This paper investigates a SAGIN in which low Earth orbit (LEO) satellite constellations communicate with multiple ground hotspots via RIS-assisted UAV relays, serving both FAS-equipped and conventional users. A system model is developed that explicitly captures satellite mobility, UAV trajectories, RIS phase control, and heterogeneous user reception capabilities. Accordingly, a multi-hotspot downlink rate maximization problem is studied, whose solvability is analyzed through a hierarchical Stackelberg game. To address heterogeneous and time-varying multi-hotspot environments, an adaptive personalized federated reinforcement learning (FRL) algorithm is proposed for adaptive optimization of UAV trajectories and RIS phase controls. Simulation results demonstrate superior performance and validate the effectiveness of personalization in dynamic heterogeneous SAGIN scenarios.

翻译：空天地一体化网络（SAGINs）通过互联卫星、无人机（UAVs）和地面设备，以实现灵活且泛在的无线服务。可重构智能表面（RISs）与流体天线系统（FASs）的集成进一步增强了无线环境的可控性。然而，跨层设施与无线增强技术的紧密集成导致了显著的环境动态性和异构性，这给大规模SAGIN中的系统建模与优化带来了根本性挑战。本文研究了一种SAGIN场景，其中低地球轨道（LEO）卫星星座通过RIS辅助的无人机中继与多个地面热点区域通信，为同时配备FAS的用户和传统用户提供服务。本文建立了一个系统模型，该模型明确刻画了卫星移动性、无人机轨迹、RIS相位控制以及异构用户接收能力。相应地，研究了一个多热点下行链路速率最大化问题，并通过一个分层Stackelberg博弈分析了其可解性。为应对异构且时变的多热点环境，本文提出了一种自适应个性化联邦强化学习（FRL）算法，用于对无人机轨迹和RIS相位控制进行自适应优化。仿真结果展示了优越的性能，并验证了在动态异构SAGIN场景中个性化策略的有效性。