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.

TinyMetaFed 是一个模型不可知的元学习框架，“联邦元学习” 可以减少标记数据的缺乏和现实世界中分布不均的问题，并在计算效率、通信和隐私方面具有优势，可以用于解决小型设备上的 TinyML 模型。

高效的针对 TinyML 的联邦元学习算法 TinyMetaFed

TinyMetaFed: Efficient Federated Meta-Learning for TinyML

Many IoT applications at the network edge demand intelligent decisions in a
real-time manner. The edge device alone, however, often cannot achieve
real-time edge intelligence due to its constrained computing resources and
limited local data. To tackle these challenges, we propose a platform-aided
collaborative learning framework where a model is first trained across a set of
source edge nodes by a federated meta-learning approach, and then it is rapidly
adapted to learn a new task at the target edge node, using a few samples only.
Further, we investigate the convergence of the proposed federated meta-learning
algorithm under mild conditions on node similarity and the adaptation
performance at the target edge. To combat against the vulnerability of
meta-learning algorithms to possible adversarial attacks, we further propose a
robust version of the federated meta-learning algorithm based on
distributionally robust optimization, and establish its convergence under mild
conditions. Experiments on different datasets demonstrate the effectiveness of
the proposed Federated Meta-Learning based framework.

提出了一种基于联邦元学习的平台辅助协作学习框架，用于解决物联网边缘节点受限计算资源和数据局限性的问题，并实现了针对边缘目标节点的快速任务适应。此外，还提出了基于分布式鲁棒优化的鲁棒性联邦元学习算法，并在数据集上展示了其有效性。