Edge2Train: a framework to train machine learning models (SVMs) on resource-constrained IoT edge devices

In recent years, ML (Machine Learning) models that have been trained in data centers can often be deployed for use on edge devices. When the model deployed on these devices encounters unseen data patterns, it will either not know how to react to that specific scenario or result in a degradation of accuracy. To tackle this, in current scenarios, most edge devices log such unseen data in the cloud via the internet. Using this logged data, the initial ML model is then re-trained/upgraded in the data center and then sent to the edge device as an OTA (Over The Air) update. When applying such an online approach, the cost of edge devices increases due to the addition of wireless modules (4G or WiFi) and it also increases the cyber-security risks. Additionally, it also requires maintaining a continuous connection between edge devices and the cloud infrastructure leading to the requirement of high network bandwidth and traffic. Finally, such online devices are not self-contained ubiquitous systems. In this work, we provide Edge2Train, a framework which enables resource-scarce edge devices to re-train ML models locally and offline. Thus, edge devices can continuously improve themselves for better analytics results by managing to understand continuously evolving real-world data on the fly. In this work, we provide algorithms for Edge2Train along with its C++ implementations. Using these functions, on-board, offline SVM training, inference, and evaluation has been performed on five popular MCU boards. The results show that our Edge2Train-trained SVMs produce classification accuracy close to that of SVMs trained on high resource setups. It also performs unit inference for values with 64-dimensional features 3.5x times faster than CPUs, while consuming only 1/350^th of the energy that CPUs consume.