  Edge computing is the natural progression from Cloud computing, where,
instead of collecting all data and processing it centrally, like 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. There are various
reasons this model is being adopted quickly, including privacy, and reduced
power and bandwidth requirements on the Edge nodes. While it is common to see
inference being done on Edge nodes today, it is much less common to do training
on the Edge. The reasons for this range from computational limitations, to it
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, as well as the use of checkpointing strategies to save memory.
