  Mega-constellations of small-size Low Earth Orbit (LEO) satellites are
currently planned and deployed by various private and public entities. While
global connectivity is the main rationale, these constellations also offer the
potential to gather immense amounts of data, e.g., for Earth observation. Power
and bandwidth constraints together with motives like privacy, limiting delay,
or resiliency make it desirable to process this data directly within the
constellation. We consider the implementation of on-board federated learning
(FL) orchestrated by an out-of-constellation parameter server (PS) and propose
a novel communication scheme tailored to support FL. It leverages intra-orbit
inter-satellite links, the predictability of satellite movements and partial
aggregating to massively reduce the training time and communication costs. In
particular, for a constellation with 40 satellites equally distributed among
five low Earth orbits and the PS in medium Earth orbit, we observe a 29x
speed-up in the training process time and a 8x traffic reduction at the PS over
the baseline.
