  Low Earth Orbit (LEO) satellite constellations have seen a surge in
deployment over the past few years by virtue of their ability to provide
broadband Internet access as well as to collect vast amounts of Earth
observational data that can be utilized to develop AI on a global scale. As
traditional machine learning (ML) approaches that train a model by downloading
satellite data to a ground station (GS) are not practical, Federated Learning
(FL) offers a potential solution. However, existing FL approaches cannot be
readily applied because of their excessively prolonged training time caused by
the challenging satellite-GS communication environment. This paper proposes
FedHAP, which introduces high-altitude platforms (HAPs) as distributed
parameter servers (PSs) into FL for Satcom (or more concretely LEO
constellations), to achieve fast and efficient model training. FedHAP consists
of three components: 1) a hierarchical communication architecture, 2) a model
dissemination algorithm, and 3) a model aggregation algorithm. Our extensive
simulations demonstrate that FedHAP significantly accelerates FL model
convergence as compared to state-of-the-art baselines, cutting the training
time from several days down to a few hours, yet achieving higher accuracy.
