Data selection and storage on the edge for efficient edge learning

A system comprising:

• one or more computing nodes;
• one or more sensors;
• and
• an edge device in electrical communication with the one or more computing nodes and the one or more sensors, where the edge device is operative to:
• obtain a machine learning model from the one or more cloud computing nodes;
• obtain learning data from the one or more sensors;
• train the machine learning model at the edge device using learning data obtained at the edge device;
• process the machine learning model to derive a scoring vector for the machine learning model;
• derive a similarity matrix from the learning data and the scoring vector, the similarity matrix comprising a plurality of cells indicative of a similarity between data of different data types and a series of elements in the scoring vector;
• and
• generate a list of selected data of the learning data to be stored at the edge device based at least on the similarity matrix.

A computer-readable storage medium containing program instructions for a method being executed by an application, the application comprising code for one or more components that are called by the application during runtime, wherein execution of the program instructions by one or more processors of a computer system causes the one or more processors to perform steps comprising:

• training a machine learning model at an edge device using learning data obtained at the edge device, each of the learning data comprising a data type that is part of a feature space;
• processing the machine learning model to derive a scoring vector for the machine learning model, the scoring vector comprising a series of elements specifying a relevance of each data type for training the machine learning model;
• deriving a similarity matrix from the learning data and the scoring vector, the similarity matrix comprising a plurality of cells indicative of a similarity between data of different data types and the series of elements in the scoring vector;
• generating a list of selected data of the learning data to be stored at the edge device based at least on the similarity matrix;
• storing a subset of the learning data according to the list of selected data;
• and
• further training the machine learning model using the subset of the learning data stored at the edge device.