The field of Tiny Machine Learning (TinyML) has made substantial advancements in democratizing machine learning on low-footprint devices, such as microcontrollers. The prevalence of these miniature devices raises the question of whether aggregating their knowledge can benefit TinyML applications. Federated meta-learning is a promising answer to this question, as it addresses the scarcity of labeled data and heterogeneous data distribution across devices in the real world. However, deploying TinyML hardware faces unique resource constraints, making existing methods impractical due to energy, privacy, and communication limitations. We introduce TinyMetaFed, a model-agnostic meta-learning framework suitable for TinyML. TinyMetaFed facilitates collaborative training of a neural network initialization that can be quickly fine-tuned on new devices. It offers communication savings and privacy protection through partial local reconstruction and Top-P% selective communication, computational efficiency via online learning, and robustness to client heterogeneity through few-shot learning. The evaluations on three TinyML use cases demonstrate that TinyMetaFed can significantly reduce energy consumption and communication overhead, accelerate convergence, and stabilize the training process.

翻译：微型机器学习（TinyML）领域在推动低功耗设备（如微控制器）上的机器学习民主化方面取得了重大进展。这些微型设备的普及引发了问题：聚合它们所学的知识是否能惠及TinyML应用？联邦元学习是这个问题的一个有前景的答案，因为它解决了实际中设备上标记数据稀缺和异构数据分布的问题。然而，部署TinyML硬件面临独特的资源约束，现有的方法因能量、隐私和通信限制而变得不切实际。我们提出了TinyMetaFed，一个适用于TinyML的模型无关元学习框架。TinyMetaFed促进了神经网络初始化的协作训练，使得该初始化可以在新设备上快速微调。它通过局部部分重构和Top-P%选择性通信节省通信资源并保护隐私，通过在线学习提高计算效率，并通过少样本学习增强对客户端异构性的稳健性。在三种TinyML用例上的评估表明，TinyMetaFed能显著降低能耗和通信开销，加速收敛，并稳定训练过程。