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SatDL: Jointly Optimizing Data Redistribution and Training for Satellite-Based Distributed Learning

arXiv · AI, language, vision and robotics · article · Aug 25, 2026 · UTC

Satellite-based distributed learning promises to train machine-learning models directly in orbit using massive, globally dispersed sensor data, thereby avoiding large-scale data downloads to ground servers. However, training convergence is significantly slowed by severe non-IID data, specifically label imbalance, as each satellite observes different geographic regions with distinct labels. This imbalance extends training duration and increases energy consumption for solar-powered satellites. Existing approaches either fully redistribute data to enforce IID conditions - accelerating convergence

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