SatFed: A Resource-Efficient LEO-Satellite-Assisted Heterogeneous Federated Learning Framework

Traditional federated learning (FL) frameworks rely heavily on terrestrial networks, whose 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 the 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, improving local training in heterogeneous environments. Extensive experiments with real-world LEO satellite networks demonstrate that SatFed achieves superior performance and robustness compared with state-of-the-art benchmarks.

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