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Mega-constellations of small-size Low Earth Orbit (LEO) satellites arecurrently planned and deployed by various private and public entities. Whileglobal connectivity is the main rationale, these constellations also offer thepotential to gather immense amounts of data, e.g., for Earth observation. Powerand bandwidth constraints together with motives like privacy, limiting delay,or resiliency make it desirable to process this data directly within theconstellation. We consider the implementation of on-board federated learning(FL) orchestrated by an out-of-constellation parameter server (PS) and proposea novel communication scheme tailored to support FL. It leverages intra-orbitinter-satellite links, the predictability of satellite movements and partialaggregating to massively reduce the training time and communication costs. Inparticular, for a constellation with 40 satellites equally distributed amongfive low Earth orbits and the PS in medium Earth orbit, we observe a 29xspeed-up in the training process time and a 8x traffic reduction at the PS overthe baseline.

On-Board Federated Learning for Dense LEO Constellations