空天地一体化网络中的联邦学习编排：自适应数据卸载与无缝切换
Orchestrating Federated Learning in Space-Air-Ground Integrated Networks: Adaptive Data Offloading and Seamless Handover
AI总结：
针对偏远地区无地面基站、设备算力不足导致机器学习服务难以落地的问题，提出适配空天地一体化网络的联邦学习方法，利用空天节点兼做边缘计算与模型聚合单元，设计自适应数据卸载与切换流程，经实验验证在训练时间和测试精度上优于多种基线。
摘要：
偏远地区的设备往往缺乏完善的地面通信基础设施覆盖，这不仅导致它们无法享受高质量的通信服务，还阻碍了机器学习服务在偏远地区的落地。本文提出了一种适配空天地一体化网络（SAGINs）的新型联邦学习（FL）方法以解决该问题。我们的方法策略性地将空间层与空中层的节点同时用作联邦学习过程中的（i）边缘计算单元与（ii）模型聚合器，从而应对目标区域内地面设备计算能力有限、且无地面基站的挑战。该方法的核心思路是结合SAGINs中的多种网络动态特性设计自适应数据卸载与切换流程，这些特性包括卫星的移动性、异构计算能力以及不一致的覆盖时长。我们分析了所提方案的时延，开发了自适应数据卸载优化器，还刻画了所提算法的理论收敛界。实验结果证实，与多种基线方法相比，我们的SAGIN辅助联邦学习方法在训练时间和测试精度方面均具备优势。
英文摘要：
Devices located in remote regions often lack coverage from well-developed terrestrial communication infrastructure. This not only prevents them from experiencing high quality communication services but also hinders the delivery of machine learning services in remote regions. In this paper, we propose a new federated learning (FL) methodology tailored to space-air-ground integrated networks (SAGINs) to tackle this issue. Our approach strategically leverages the nodes within space and air layers as both (i) edge computing units and (ii) model aggregators during the FL process, addressing the challenges that arise from the limited computation powers of ground devices and the absence of terrestrial base stations in the target region. The key idea behind our methodology is the adaptive data offloading and handover procedures that incorporate various network dynamics in SAGINs, including the mobility, heterogeneous computation powers, and inconsistent coverage times of incoming satellites. We analyze the latency of our scheme and develop an adaptive data offloading optimizer, and also characterize the theoretical convergence bound of our proposed algorithm. Experimental results confirm the advantage of our SAGIN-assisted FL methodology in terms of training time and test accuracy compared with various baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
