Presentation
Towards Fast and Robust Split Federated Learning over Satellite-Based Computing Networks
DescriptionTraining satellite-edge deep learning models remains constrained by limited onboard resources and high data download latency. Although split federated learning (SFL) offers a potential solution through model partitioning, its convergence and robustness are fundamentally compromised by the intermittent and asymmetric satellite–ground links. To address these issues, we introduce SatSFL as a novel SFL system for satellite-based computing networks. SatSFL employs an interpolated gradient approximation to emulate ground feedback during disconnections, markedly accelerating convergence while maintaining robustness under heterogeneous data. In addition, we design adaptive uplink compression under asymmetric bandwidth to ensure that balanced and critical gradients are reliably transmitted back to satellites. We implement and evaluate SatSFL on real-world LEO satellite systems and datasets, demonstrating superior accuracy and convergence speed compared to state-of-the-art methods.
Event Type
Research Manuscript
TimeWednesday, July 2912:16pm - 12:30pm PDT
LocationMtg Room 101B
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