  In Low Earth Orbit (LEO) mega constellations, there are relevant use cases,
such as inference based on satellite imaging, in which a large number of
satellites collaboratively train a machine learning model without sharing their
local datasets. To address this problem, we propose a new set of algorithms
based on Federated learning (FL), including a novel asynchronous FL procedure
based on FedAvg that exhibits better robustness against heterogeneous scenarios
than the state-of-the-art. Extensive numerical evaluations based on MNIST and
CIFAR-10 datasets highlight the fast convergence speed and excellent asymptotic
test accuracy of the proposed method.
