Editing Machine Learning at the Edge (section)

=='''4.1 Overview of ML at the Edge'''==
==='''Introduction'''===
Machine Learning is a branch of Artificial Intelligence that is primarily concerned with the training of algorithms to look at sets of data, analyze patterns, and generate conclusions so that the gathered data can be used to generate results and carry out tasks. As an application, machine learning is especially relevant when considering edge computing [7]. Given the recent prevalence and rise in the field of machine learning, it is inevitable that deployment of machine learning algorithms will play a crucial role in the function of edge computing systems as well. The abundant sensor networks often involved in edge systems provide a means of gathering very large amounts of data. With the help of machine learning, such data can be utilized in very effective ways and automation methods can be employed to improve efficiency. Machine learning on edge devices themselves can help ease the burden on cloud systems and the networks connected to them as well, especially if the model does need such large computational power such as in the case of certain Small Language Models (SLMs). However, the main issue regarding machine learning at the edge lies in the devices, which may have limited computational power, and the environment, which may change dynamically and be effected by network congestion, device outages, or other unexpected events. If these challenges can be overcome, edge systems could play a pivotal role in advancing machine learning and utilize it to maximize efficiency.

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==='''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].

==='''Challenges of Machine Learning Using Edge Computing'''===

'''Potentially Low Computational Power:''' Many edge devices lack the computational power needed for deep learning applications, especially given the large amount of data and complex operations that may have to take place [1].

'''Energy Management:''' Given that edge devices may consist of sensors, or other devices that are meant to have low energy consumption, the tasks associated with machine learning could quickly drain their power, even if they have sufficient power to run the workloads [6].

'''New Attack Surfaces:''' With more devices comes the increased potential for malicious hackers to steal data or compromise devices, despite the enhanced privacy. Encryption, access controls, and other methodologies must be employed to mitigate the potential for new attack surfaces to be exploited [6].

==='''Applications for ML at the Edge'''===
The utilization of data to make conjectures about what is going on in the environment and how to respond has a variety of use cases that can greatly benefit people, cities, and the environment. By leveraging and monitoring a constant stream of data and training machine learning models to detect or even respond to different events, there can be many practical applications for such systems. These would rely on the combination of edge devices and machine learning to better enhance experience for users and detect events of interest.

'''Self-Driving Cars''': Imitation learning can be leveraged to better understand and emulate human driving. The low latency that can be provided by edge computing is especially useful for the quick decision making needed by these systems [7].

'''Smart Home Devices''': Understanding user habits by leveraging the available data for them can make smart homes more convenient for users. The increased privacy that can come with edge computing, along with a few extra cybersecurity measures, can ensure the personal data that may be used for training is not compromised.

'''Environmental and Industrial Monitoring:''' Sensors deployed in environments and industrial settings could be trained to recognize when there are anomalies or undesirable behavior, thus ensuring a quick response and active information sending [7].

'''Smart Cities:''' Similar to above, the data collected by sensors in cities can leverage machine learning to help in crime and emergency detection, traffic management, or energy management[7].