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）上的机器学习普及方面取得了重大进展。这类微型设备的广泛存在引出一个问题：聚合它们的知识能否为TinyML应用带来增益。联邦元学习为这一问题提供了有前景的解决方案，因为它能应对现实世界中设备间标签数据稀缺和异构数据分布的双重挑战。然而，部署TinyML硬件面临独特的资源约束，现有方法因能耗、隐私和通信限制而难以实际应用。本文提出TinyMetaFed，一种适用于TinyML的模型无关元学习框架。该框架支持神经网络初始化参数的协同训练，并能在新设备上快速微调。通过部分本地重构和Top-P%选择性通信实现通信节省与隐私保护，通过在线学习保证计算效率，并通过小样本学习增强对客户端异构性的鲁棒性。在三个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。