Deploying machine learning models on compute-constrained devices has become a key building block of modern IoT applications. In this work, we present a compression scheme for boosted decision trees, addressing the growing need for lightweight machine learning models. Specifically, we provide techniques for training compact boosted decision tree ensembles that exhibit a reduced memory footprint by rewarding, among other things, the reuse of features and thresholds during training. Our experimental evaluation shows that models achieved the same performance with a compression ratio of 4-16x compared to LightGBM models using an adapted training process and an alternative memory layout. Once deployed, the corresponding IoT devices can operate independently of constant communication or external energy supply, and, thus, autonomously, requiring only minimal computing power and energy. This capability opens the door to a wide range of IoT applications, including remote monitoring, edge analytics, and real-time decision making in isolated or power-limited environments.

翻译：在计算资源受限的设备上部署机器学习模型已成为现代物联网应用的关键组成部分。本研究提出了一种针对增强决策树的压缩方案，以应对对轻量级机器学习模型日益增长的需求。具体而言，我们提供了一种训练紧凑型增强决策树集成的方法，通过奖励训练过程中特征与阈值的重用等方式，显著降低了模型的内存占用。实验评估表明，采用改进的训练流程和替代内存布局后，所获模型在性能相当的前提下，相较于LightGBM模型实现了4至16倍的压缩比。部署后，相应的物联网设备可在无需持续通信或外部能源供应的情况下自主运行，仅需极少的计算能力和能耗。这一能力为广泛的物联网应用开辟了道路，包括远程监测、边缘分析以及在隔离或电力受限环境中的实时决策。