Long-term privacy leakage in asynchronous federated learning over LEO satellite networks is kept bounded by fixed jointly-visible satellite partitions used with secure aggregation.
Decentralised Semi-supervised Onboard Learning for Scene Classification in Low-Earth Orbit
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abstract
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.
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When Secure Aggregation Falls Short: Achieving Long-Term Privacy in Asynchronous Federated Learning for LEO Satellite Networks
Long-term privacy leakage in asynchronous federated learning over LEO satellite networks is kept bounded by fixed jointly-visible satellite partitions used with secure aggregation.