  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.
