  Onboard machine learning on the latest satellite hardware offers the
potential for significant savings in communication and operational costs. We
showcase the training of a machine learning model on a satellite constellation
for scene classification using semi-supervised learning while accounting for
operational constraints such as temperature and limited power budgets based on
satellite processor benchmarks of the neural network. We evaluate mission
scenarios employing both decentralised and federated learning approaches. All
scenarios achieve convergence to high accuracy (around 91% on EuroSAT RGB
dataset) within a one-day mission timeframe.
