This concept paper highlights a recently opened opportunity for large scale analytical algorithms to be trained directly on edge devices. Such approach is a response to the arising need of processing data generated by natural person (a human being), also known as personal data. Spiking Neural networks are the core method behind it: suitable for a low latency energy-constrained hardware, enabling local training or re-training, while not taking advantage of scalability available in the Cloud.
@article{arxiv.1912.09083,
title = {Spiking Networks for Improved Cognitive Abilities of Edge Computing Devices},
author = {Anton Akusok and Kaj-Mikael Björk and Leonardo Espinosa Leal and Yoan Miche and Renjie Hu and Amaury Lendasse},
journal= {arXiv preprint arXiv:1912.09083},
year = {2019}
}