Editing Machine Learning at the Edge (section)

==='''Edge and Cloud Collaboration'''===
One methodology that is often used involves collaboration between both Edge and Cloud Devices. The cloud has the ability to process workloads that may require much more resources and cannot be done on edge devices. On the other hand, edge devices, which can store and process data locally, may have lower latency and more privacy. Given the advantages of each of these, many have proposed that the best way to handle machine learning is through a combination of edge and cloud computing. 

The primary issue facing this computing paradigm, however, is the problem of optimally selecting which workloads should be done on the cloud and which should be done on the edge. This is a crucial problem to solve, as the correct partition of workloads is the best way to ensure that the respective benefits of the devices can be leveraged. A common way to do this, is to run certain computing tasks on the necessary devices and determine the length of time and resources that it takes. An example of this is the profiling step done in [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10818760&tag=1 EdgeShard] [1] and [https://dl.acm.org/doi/pdf/10.1145/3093337.3037698 Neurosurgeon] [4]. Other frameworks implement similar steps, where the capabilities of different devices are tested in order to allocate their workloads and determine the limit at which they can provide efficient functionality. If the workload is beyond the limits of the devices, it can be sent to the cloud for processing

The key advantage of this is that it is able to utilize the resources of the edge devices as necessary, allowing increased data privacy and lower latency. Since workloads are only processed in the cloud as needed, this will reduce the overall amount of time needed for processing because data is not constantly sent back and forth. It also allows for much less network congestion, which is crucial for many applications.

[[File:ECcollab.png|400px|thumb|center|The collaboration of Edge and Cloud Devices]]