Editing Machine Learning at the Edge (section)

==='''Benefits of Machine Learning using Edge Computing'''===
'''Data Volumes:''' Traditional cloud computing architecture may not be able to keep up with the massive volume of data that is often generated by IoT devices and sensors, thus increasing costs as well as pressure and congestion on the networks transmitting data [5]. With edge computing architectures, machine learning can be deployed in a distributed nature, allowing ML models to be deployed and trained across many edge devices. The nature of edge devices sharing computational tasks makes it perfect for taking large datasets and splitting the computational load so that many devices can compute what a single device might not have been able to handle [5]. More than just splitting input data across edge devices, machine learning models can be split up across edge devices as well. This allows for deployment of models that would be otherwise too large to be deployed on a single edge device with limited memory. In such cases, edge nodes are able to collaboratively pass data between them, and the global ML model is later updated based on the workings of each smaller portion [5]. Additionally, the sheer amount of data that processed from edge devices means more training data for machine learning models, which generally increases accuracy, and thus edge computing could be an effective solution to providing lots of good and relevant data for a variety of applications. 

'''Lower Latency:''' For certain real-time applications, such as Virtual and Augmented Reality(VR/AR) or smart cars, the latency required to transmit data and process it to the cloud may be too high to make these applications efficient and safe. A key benefit of edge computing lies in the reduced latency provided by putting devices closer to the users, and this is applicable to machine learning as well [5].

'''Enhanced privacy:''' Processing data locally rather than on the cloud reduces the risk of data theft and enhances privacy. The data does not have to be sent to anyone else, or go over a network to potentially get compromised. This is crucial given the large amounts of data needed for machine learning, and especially if personal data is needed for a personal application, many users would prefer the privacy done by having it processed locally [5].