  Low Earth Orbit (LEO) constellations, each comprising a large number of
satellites, have become a new source of big data "from the sky". Downloading
such data to a ground station (GS) for big data analytics demands very high
bandwidth and involves large propagation delays. Federated Learning (FL) offers
a promising solution because it allows data to stay in-situ (never leaving
satellites) and it only needs to transmit machine learning model parameters
(trained on the satellites' data). However, the conventional, synchronous FL
process can take several days to train a single FL model in the context of
satellite communication (Satcom), due to a bottleneck caused by straggler
satellites. In this paper, we propose an asynchronous FL framework for LEO
constellations called AsyncFLEO to improve FL efficiency in Satcom. Not only
does AsynFLEO address the bottleneck (idle waiting) in synchronous FL, but it
also solves the issue of model staleness caused by straggler satellites.
AsyncFLEO utilizes high-altitude platforms (HAPs) positioned "in the sky" as
parameter servers, and consists of three technical components: (1) a
ring-of-stars communication topology, (2) a model propagation algorithm, and
(3) a model aggregation algorithm with satellite grouping and staleness
discounting. Our extensive evaluation with both IID and non-IID data shows that
AsyncFLEO outperforms the state of the art by a large margin, cutting down
convergence delay by 22 times and increasing accuracy by 40%.
