Problem
Research questions and friction points this paper is trying to address.
Satellite-based distributed learning
non-IID data
label imbalance
training convergence
energy consumption
SatDL: Jointly Optimizing Data Redistribution and Training for Satellite-Based Distributed Learning
🤖 AI Summary
SatDL通过联合优化数据重分配和训练过程，解决卫星分布式学习中由于非IID数据导致的训练收敛慢和能耗高问题。
📝 Abstract
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 local learning algorithms to mitigate the impact of label imbalance, which, however, still prolong training and increase energy use. Both extremes result in excessive total end-to-end learning time (data-transfer delay plus training time) and thus elevated onboard energy consumption.
We present SatDL, a data-redistribution framework designed to minimize total end-to-end learning time. At its core, SatDL develops a Distributor-Critic framework that jointly models and optimizes data-transfer delay and training time. Evaluations through trace-driven simulations of a 1,584-satellite Starlink constellation and hardware emulations using NVIDIA Jetson and A100 GPUs across five datasets show SatDL reduces total end-to-end learning time by up to 18.6% and onboard energy consumption by 12.23-88.00%, while maintaining inference accuracy within a few percentage points of state-of-the-art baselines.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Satellite-based Distributed Learning
Data Redistribution
Distributor-Critic Framework
End-to-End Learning Time Optimization
🔎 Similar Papers
No similar papers found.
