FedTinyWolf—A Memory Efficient Federated Embedded Learning Mechanism

Abstract

Embedded intelligence is a challenging field in engineering given its resource-constrained environment which regular machine learning algorithms demand. Most embedded intelligence models are trained on a computer and then the learned parameters are transferred to the embedded devices to enable decision making. Although training the model within a microcontroller is possible, the state-of-the-art method requires further optimization. Moreover, federated learning (FL) is used in the state of the art to protect data privacy while training a deep learning model at edge level. Embedded learning models require memory enhancements to improve on-device FL. In this experiment, we have performed memory enhancement of gray wolf optimizer after finding it suitable for the purpose and implemented it to create edge-level, resource-efficient, data privacy preserving on-device federated training of embedded intelligence models. The performances are benchmarked on 13 open-sourced datasets showing a mean 10.8% accuracy enhancement.