Training on the Edge: The why and the how

Edge computing is a natural progression from cloud computing, where, instead of collecting all data and processing it centrally, as in a cloud computing environment, we distribute the computing power and try to do as much processing as possible, close to the source of the data. This model is being adopted quickly for various reasons, including privacy, and reduced power and bandwidth requirements on the edge nodes. While inference is commonly being done on edge nodes today, training on the edge is much less common. The reasons range from computational limitations, to its not being advantageous in reducing communications between the edge nodes. In this paper, we explore some scenarios where it is advantageous to do training on the edge, and the use of checkpointing strategies to save memory.