SatDL: Jointly Optimizing Data Redistribution and Training for Satellite-Based Distributed Learning
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 but incurring substantial communication delays - or avoid redistribution entirely by modifying l...
| Authors | Hao Wu, Kin Whye Chew, Yizhan Han et al. |
|---|---|
| Primary category | cs.DC |
| Published | 25 August 2026 |
| Last updated at source | 31 August 2026 |
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