MACHINE LEARNING AT EDGE DEVICES BASED ON DISTRIBUTED FEEDBACK

摘要

Machine learning (ML) is provided at edge computing devices based on distributed feedback received from the edge computing devices. A trained instance of an ML model is received at the edge computing devices via communications networks from an ML model manager. Feedback data including labeled observations is generated by the execution of the trained instance of the ML model at the edge computing devices on unlabeled observations captured by the edge computing devices. The feedback data is transmitted from the edge computing devices to a machine learning model manager. A re-trained instance of the machine learning model is generated from the trained instance using the collected feedback data. The re-trained instance of the machine learning model is received at the edge computing devices from the machine learning model manager. The re-trained instance of the machine learning model is executed at the edge computing devices.