  Data collected by IoT devices are often private and have a large diversity
across users. Therefore, learning requires pre-training a model with available
representative data samples, deploying the pre-trained model on IoT devices,
and adapting the deployed model on the device with local data. Such an
on-device adaption for deep learning empowered applications demands data and
memory efficiency. However, existing gradient-based meta learning schemes fail
to support memory-efficient adaptation. To this end, we propose p-Meta, a new
meta learning method that enforces structure-wise partial parameter updates
while ensuring fast generalization to unseen tasks. Evaluations on few-shot
image classification and reinforcement learning tasks show that p-Meta not only
improves the accuracy but also substantially reduces the peak dynamic memory by
a factor of 2.5 on average compared to state-of-the-art few-shot adaptation
methods.
