  An increasing number of artificial intelligence (AI) applications involve the
execution of deep neural networks (DNNs) on edge devices. Many practical
reasons motivate the need to update the DNN model on the edge device
post-deployment, such as refining the model, concept drift, or outright change
in the learning task. In this paper, we consider the scenario where retraining
can be done on the server side based on a copy of the DNN model, with only the
necessary data transmitted to the edge to update the deployed model. However,
due to bandwidth constraints, we want to minimise the transmission required to
achieve the update. We develop a simple approach based on matrix factorisation
to compress the model update -- this differs from compressing the model itself.
The key idea is to preserve existing knowledge in the current model and
optimise only small additional parameters for the update which can be used to
reconstitute the model on the edge. We compared our method to similar
techniques used in federated learning; our method usually requires less than
half of the update size of existing methods to achieve the same accuracy.
