Traditional federated learning (FL) frameworks rely heavily on terrestrial networks, where coverage limitations and increasing bandwidth congestion significantly hinder model convergence. Fortunately, the advancement of low-Earth orbit (LEO) satellite networks offers promising new communication avenues to augment traditional terrestrial FL. Despite this potential, the limited satellite-ground communication bandwidth and the heterogeneous operating environments of ground devices-including variations in data, bandwidth, and computing power-pose substantial challenges for effective and robust satellite-assisted FL. To address these challenges, we propose SatFed, a resource-efficient satellite-assisted heterogeneous FL framework. SatFed implements freshness-based model prioritization queues to optimize the use of highly constrained satellite-ground bandwidth, ensuring the transmission of the most critical models. Additionally, a multigraph is constructed to capture real-time heterogeneous relationships between devices, including data distribution, terrestrial bandwidth, and computing capability. This multigraph enables SatFed to aggregate satellite-transmitted models into peer guidance, enhancing local training in heterogeneous environments. Extensive experiments with real-world LEO satellite networks demonstrate that SatFed achieves superior performance and robustness compared to state-of-the-art benchmarks.

翻译：传统联邦学习（FL）框架严重依赖地面网络，其覆盖范围限制和日益增长的带宽拥塞显著阻碍了模型收敛。幸运的是，低地球轨道（LEO）卫星网络的发展为增强传统地面联邦学习提供了前景广阔的新通信途径。尽管存在这一潜力，有限的星地通信带宽以及地面设备（包括数据、带宽和计算能力方面的差异）所处的异构运行环境，对实现高效且鲁棒的卫星辅助联邦学习构成了重大挑战。为应对这些挑战，我们提出了SatFed，一种资源高效的卫星辅助异构联邦学习框架。SatFed实现了基于模型新鲜度的优先级队列，以优化利用高度受限的星地带宽，确保传输最关键模型。此外，构建了一个多图来捕捉设备间实时的异构关系，包括数据分布、地面带宽和计算能力。该多图使SatFed能够将卫星传输的模型聚合为对等指导，从而增强异构环境下的本地训练。利用真实LEO卫星网络进行的大量实验表明，与现有先进基准方法相比，SatFed实现了更优的性能和鲁棒性。