发表机构
East China Normal University; Institute of Computing Technology, Chinese Academy of Sciences; Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences; Tsinghua University（华东师范大学; 中国科学院计算技术研究所; 中国科学院大学杭州高等研究院; 清华大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对卫星辅助联邦学习中部分设备参与导致资源闲置和数据异构问题，提出持续计算框架CoCoFL，让未调度设备继续本地更新，并联合优化调度和本地轮数，实现更快收敛和更高精度。
AI 中文摘要
低地球轨道（LEO）卫星星座使地理上分布的地面设备能够通过联邦学习（FL）协作训练全局模型，而无需共享原始数据，在环境监测和灾害预测中具有应用前景。然而，在卫星辅助的FL场景中，间歇性星地链路仅允许每个可见窗口内的一部分设备参与全局聚合，导致未调度的设备空闲，其本地计算和数据资源得不到充分利用。在部分设备参与的情况下，设备间的数据异构性可能使全局模型偏向某些设备，从而降低学习性能。为此，我们提出了一种基于持续计算的联邦学习框架，称为CoCoFL，其中被调度的设备参与全局模型聚合，而未调度的设备则考虑模型陈旧性继续更新其本地模型。在CoCoFL的收敛性分析指导下，并受可见窗口相关时间约束的限制，我们联合优化了设备调度以及调度和未调度设备的本地轮数。实验结果表明，与基线相比，CoCoFL实现了更快的收敛、更低的训练损失和更高的测试准确率。
英文摘要
Low earth orbit (LEO) satellite constellations enable geographically distributed ground devices to collaboratively train a global model via federated learning (FL) without sharing raw data, with applications in environmental monitoring and disaster prediction. However, in satellite-assisted FL scenarios, intermittent satellite-ground links allow only a subset of devices to participate in global aggregation within each visibility window, leaving unscheduled devices idle and their local computational and data resources underutilized. Under partial device participation, data heterogeneity among devices may bias the global model toward certain devices, thereby deteriorating learning performance. In this regard, we propose a continual computing based federated learning framework, referred to as CoCoFL, in which scheduled devices participate in the global model aggregation, while unscheduled devices continue updating their local models taking into account model staleness. Guided by the convergence analysis of CoCoFL and subject to visible-window-related time constraints, we jointly optimize the device scheduling and the number of local epochs for scheduled and unscheduled devices. Experimental results demonstrate that CoCoFL achieves faster convergence, lower training loss, and higher test accuracy compared with baselines.
