  Recently, a large number of Low Earth Orbit (LEO) satellites have been
launched and deployed successfully in space by commercial companies, such as
SpaceX. Due to multimodal sensors equipped by the LEO satellites, they serve
not only for communication but also for various machine learning applications,
such as space modulation recognition, remote sensing image classification, etc.
However, the ground station (GS) may be incapable of downloading such a large
volume of raw sensing data for centralized model training due to the limited
contact time with LEO satellites (e.g. 5 minutes). Therefore, federated
learning (FL) has emerged as the promising solution to address this problem via
on-device training. Unfortunately, to enable FL on LEO satellites, we still
face three critical challenges that are i) heterogeneous computing and memory
capabilities, ii) limited uplink rate, and iii) model staleness. To this end,
we propose FedSN as a general FL framework to tackle the above challenges, and
fully explore data diversity on LEO satellites. Specifically, we first present
a novel sub-structure scheme to enable heterogeneous local model training
considering different computing, memory, and communication constraints on LEO
satellites. Additionally, we propose a pseudo-synchronous model aggregation
strategy to dynamically schedule model aggregation for compensating model
staleness. To further demonstrate the effectiveness of the FedSN, we evaluate
it using space modulation recognition and remote sensing image classification
tasks by leveraging the data from real-world satellite networks. Extensive
experimental results demonstrate that FedSN framework achieves higher accuracy,
lower computing, and communication overhead than the state-of-the-art
benchmarks and the effectiveness of each components in FedSN.
