One-Shot Federated Learning for LEO Constellations that Reduces
  Convergence Time from Days to 90 Minutes
Mohamed Elmahallawy Tie Luo
A Low Earth orbit (LEO) satellite constellation consists of a large number of
small satellites traveling in space with high mobility and collecting vast
amounts of mobility data such as cloud movement for weather forecast, large
herds of animals migrating across geo-regions, spreading of forest fires, and
aircraft tracking. Machine learning can be utilized to analyze these mobility
data to address global challenges, and Federated Learning (FL) is a promising
approach because it eliminates the need for transmitting raw data and hence is
both bandwidth and privacy-friendly. However, FL requires many communication
rounds between clients (satellites) and the parameter server (PS), leading to
substantial delays of up to several days in LEO constellations. In this paper,
we propose a novel one-shot FL approach for LEO satellites, called LEOShot,
that needs only a single communication round to complete the entire learning
process. LEOShot comprises three processes: (i) synthetic data generation, (ii)
knowledge distillation, and (iii) virtual model retraining. We evaluate and
benchmark LEOShot against the state of the art and the results show that it
drastically expedites FL convergence by more than an order of magnitude. Also
surprisingly, despite the one-shot nature, its model accuracy is on par with or
even outperforms regular iterative FL schemes by a large margin