Edge Machine Learning (Edge ML), which shifts computational intelligence from
cloud-based systems to edge devices, is attracting significant interest due to
its evident benefits including reduced latency, enhanced data privacy, and
decreased connectivity reliance. While these advantages are compelling, they
introduce unique challenges absent in traditional cloud-based approaches. In
this paper, we delve into the intricacies of Edge-based learning, examining the
interdependencies among: (i) constrained data storage on Edge devices, (ii)
limited computational power for training, and (iii) the number of learning
classes. Through experiments conducted using our MAGNETO system, that focused
on learning human activities via data collected from mobile sensors, we
highlight these challenges and offer valuable perspectives on Edge ML.

利用移动传感器收集的数据，通过我们的 MAGNETO 系统以学习人类活动为重点的实验，我们深入研究了边缘机器学习（Edge ML）的复杂性，考察了边缘设备上受限数据存储、训练的有限计算能力和学习类别数量之间的相互依赖关系，并提供了关于 Edge ML 的有价值的观点。