Editing Machine Learning at the Edge (section)

==='''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].