研究微控制器上设备部署后数据分布变化，提出针对性学习方案
What changes after deployment? A survey on On-device Learning in TinyML
- 按分布变化类型统一分析70篇边缘学习论文
- 发现真实场景与测试基准间存在显著差距
- 为不同变化类型提供适配的边缘学习设计建议
微型控制器类设备上的机器学习模型面临一个根本性挑战：部署后的数据分布变化会削弱静态模型性能。设备端学习（On-device Learning, ODL）通过在设备上直接运行学习过程来应对这一问题。现有文献尚未系统刻画分布变化的机制及其对解决方案的差异化影响。本综述对约70篇ODL工作进行梳理，基于‘分布变化范式’统一分析其对可解决应用、硬件配置及方案结构的影响。同时揭示了方法论评估基准与真实部署场景之间的持续差距。
原文摘要 · Abstract (English)
Machine learning models on microcontroller-class devices (TinyML) face a fundamental challenge: post-deployment distribution change undermines static models. On-device learning (ODL) addresses this by running the learning process directly on the device. The existing literature has not characterized how distribution change occurs or how different change types require different solutions. Approximately 70 ODL works are surveyed under one principle: the distribution change regime. The survey analyzes how different types of distribution change influence the applications addressable on-device, the hardware employed, and the structure of the solutions. A persistent gap between methodological benchmarks and real-world deployment scenarios is also identified.
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