Space information networks (SINs) have become a rapidly growing global infrastructure service. Massive volumes of high-resolution images and videos captured by low-orbit satellites and unmanned aerial vehicles have provided a rich training data source for machine learning applications. However, SIN devices' limited communication and computation resources make it challenging to perform machine learning efficiently with a swarm of SIN devices. In this article, we propose Spacedml, a distributed machine learning system for SIN platforms that applies dynamic model compression techniques to adapt distributed machine learning training to SINs' limited bandwidth and unstable connectivity. Spaced-ml has two key algorithm:s adaptive loss-aware quantization, which compresses models without sacrificing their quality, and partial weight averaging, which selectively averages active clients' partial model updates. These algorithms jointly improve communication efficiency and enhance the scalability of distributed machine learning with SIN devices. We evaluate Spacedml by training a LeNet-S model on the MNIST dataset. The experimental results show that Spacedml can increase model accuracy by 2-3 percent and reduce communication bandwidth consumption by up to 60 percent compared to the baseline algorithm.

中文翻译：

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SpaceDML：在空间信息网络中实现分布式机器学习


空间信息网络（SIN）已成为快速增长的全球基础设施服务。低轨卫星和无人机捕获的海量高分辨率图像和视频为机器学习应用提供了丰富的训练数据源。然而，SIN 设备有限的通信和计算资源使得利用大量 SIN 设备高效执行机器学习变得具有挑战性。在本文中，我们提出了 Spacedml，这是一种适用于 SIN 平台的分布式机器学习系统，它应用动态模型压缩技术来使分布式机器学习训练适应 SIN 的有限带宽和不稳定的连接。 Spaced-ml 有两个关键算法：自适应损失感知量化（在不牺牲模型质量的情况下压缩模型）和部分权重平均（有选择地平均活跃客户端的部分模型更新）。这些算法共同提高了通信效率并增强了SIN设备分布式机器学习的可扩展性。我们通过在 MNIST 数据集上训练 LeNet-S 模型来评估 Spacedml。实验结果表明，与基线算法相比，Spacedml 可以将模型精度提高 2-3%，并降低通信带宽消耗高达 60%。