Editing Machine Learning at the Edge (section)

==='''Horizontal and Vertical Partitioning'''===
There are 2 major ways that these models split the workloads in order to optimize the machine learning: Horizontal and vertical partitioning [3]. Given a set of layers that ranges from the cloud to edge, vertical partitioning involves splitting up the workload between the layers. For example, if a large amount of computational resources is deemed necessary, this task may go to the cloud to be completed and preprocessed. One the other hand, if a small amount of computational power is required, this type of work can go to edge devices. Such partitioning also depends on the confidence and accuracy level of the given learning. If the accuracy is completed on an edge device and found to be very low, it can be sent to the cloud; on the other hand if the accuracy is already fairly high and the learning model needs smaller work to reach the threshold deemed acceptable, it may be sent to edge devices to free up network traffic on the cloud and reduce latency [3].

The second model of partitioning is called horizontal partitioning. This involves splitting among the devices within a certain layer rather than among the layers themselves. This is similar to what has been described in previous sections, as it allows a means for fully utilizing the heterogenous abilities that are found in edge devices. Similar functionality and determination to what is found in horizontal partitioning is done, but all of the devices that the workload is split across function on the same layer [3]. To fully optimize a machine learning model, both horizontal and vertical partitioning must be used. 

[[File:Edge_computing_layers.png|400px|thumb|center|An example of different layers with multiple devices]]