Editing Machine Learning at the Edge (section)

==='''Transfer Learning'''===
Transfer learning is a method of machine learning in which a pretrained model is sent to the different nodes for further processing and fine-tuning. The initial training of the model may involve a very large amount of data and could place a major burden on the device that must execute it, which may not be feasible for edge devices. Therefore, the bulk of the model training is done by a more powerful machine, such as the cloud, and then the pretrained model is sent to the edge device. This can be useful, as it reduces the computational burden on the edge device, and allows it to fine-tune the model using the data it collects without having to completely train the whole system. One form of this is knowledge distillation, in which a smaller model can be trained to mimic that of a larger model. This may often be the case when edge and cloud systems are used in a combined way.