SatFed: A Resource-Efficient LEO Satellite-Assisted Heterogeneous Federated Learning Framework
<|reference_start|>SatFed: A Resource-Efficient LEO Satellite-Assisted Heterogeneous Federated Learning Framework: 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.<|reference_end|>
@article{zhang2024satfed:,
    title={SatFed: A Resource-Efficient LEO Satellite-Assisted Heterogeneous
  Federated Learning Framework},
    author={Yuxin Zhang, Zheng Lin, Zhe Chen, Zihan Fang, Wenjun Zhu, Xianhao
  Chen, Jin Zhao, Yue Gao},
    journal={arXiv preprint arXiv:2409.13503},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.13503},
    primaryClass={cs.DC cs.AI cs.LG}
}