  Recent advancements in space technology have equipped low Earth Orbit (LEO)
satellites with the capability to perform complex functions and run AI
applications. Federated Learning (FL) on LEO satellites enables collaborative
training of a global ML model without the need for sharing large datasets.
However, intermittent connectivity between satellites and ground stations can
lead to stale gradients and unstable learning, thereby limiting learning
performance. In this paper, we propose FedGSM, a novel asynchronous FL
algorithm that introduces a compensation mechanism to mitigate gradient
staleness. FedGSM leverages the deterministic and time-varying topology of the
orbits to offset the negative effects of staleness. Our simulation results
demonstrate that FedGSM outperforms state-of-the-art algorithms for both IID
and non-IID datasets, underscoring its effectiveness and advantages. We also
investigate the effect of system parameters.
