Recent advances in Tiny Machine Learning (TinyML) empower low-footprint
embedded devices for real-time on-device Machine Learning. While many
acknowledge the potential benefits of TinyML, its practical implementation
presents unique challenges. This study aims to bridge the gap between
prototyping single TinyML models and developing reliable TinyML systems in
production: (1) Embedded devices operate in dynamically changing conditions.
Existing TinyML solutions primarily focus on inference, with models trained
offline on powerful machines and deployed as static objects. However, static
models may underperform in the real world due to evolving input data
distributions. We propose online learning to enable training on constrained
devices, adapting local models towards the latest field conditions. (2)
Nevertheless, current on-device learning methods struggle with heterogeneous
deployment conditions and the scarcity of labeled data when applied across
numerous devices. We introduce federated meta-learning incorporating online
learning to enhance model generalization, facilitating rapid learning. This
approach ensures optimal performance among distributed devices by knowledge
sharing. (3) Moreover, TinyML's pivotal advantage is widespread adoption.
Embedded devices and TinyML models prioritize extreme efficiency, leading to
diverse characteristics ranging from memory and sensors to model architectures.
Given their diversity and non-standardized representations, managing these
resources becomes challenging as TinyML systems scale up. We present semantic
management for the joint management of models and devices at scale. We
demonstrate our methods through a basic regression example and then assess them
in three real-world TinyML applications: handwritten character image
classification, keyword audio classification, and smart building presence
detection, confirming our approaches' effectiveness.

近年来微型机器学习（TinyML）的最新进展为低占用嵌入式设备提供了实时的设备端机器学习能力。然而，TinyML 的实际实施面临着独特的挑战。本研究旨在弥合原型设计的单一 TinyML 模型与开发可靠的生产级 TinyML 系统之间的差距: (1) 在动态变化的条件下，嵌入式设备的操作。现有的 TinyML 解决方案主要集中于推断，使用在强大的计算机上进行离线训练的模型，并部署为静态对象。然而，在实际环境中，由于输入数据分布的演变，静态模型可能性能不佳。我们提出在线学习以实现在受限设备上的训练，并使本地模型适应最新的现场条件。(2) 然而，当前的设备端学习方法在部署条件异构和标注数据缺乏的情况下遇到困难，特别是在多设备上应用。 我们引入联邦元学习将在线学习结合在一起，以增强模型的泛化能力，促进快速学习。本方法通过知识共享确保分布式设备之间的最佳性能。(3) 此外，TinyML 的重要优势是被广泛采用。嵌入式设备和 TinyML 模型优先考虑高效率，导致资源的多样性，从内存和传感器到模型架构都具有多样性和非标准化的表示，因此在扩展 TinyML 系统时管理这些资源变得具有挑战性。 我们提出语义管理以进行模型和设备的联合管理。我们通过一个基本的回归示例演示了我们的方法，然后在三个真实的 TinyML 应用中进行评估：手写字符图像分类，关键词音频分类和智能建筑出席检测，验证了我们方法的有效性。