MACHINE LEARNING HETEROGENEOUS EDGE DEVICE, METHOD, AND SYSTEM

摘要

A machine learning heterogeneous edge device, method, and system are disclosed. In an example embodiment, an edge device includes a communication module, a data collection device, a memory, a machine learning module, a group determination module, and a leader election module. The edge device analyzes collected data with a model, outputs a result, and updates the model to create a local model. The edge device communicates with other edge devices in a heterogeneous group. The edge device determines group membership and determines a leader edge device. The edge device receives a request for the local model, transmits the local model to the leader edge device, receives a mixed model created by the leader edge device performing a mix operation of the local model and a different local model, and replaces the local model with the mixed model.

主权项

1. An edge device comprising:
a communication module configured to communicate with a plurality of different edge devices; a data collection device configured to collect a first type of data; a memory configured to store data collected by the data collection device; a machine learning module; a group determination module; and a leader election module, wherein the edge device is configured to:
analyze, using a first model relating to a predefined task, first data collected by the data collection device;output a result including at least one of a prediction, a classification, a clustering, an anomaly detection, and a recognition;update, based on a correctness of the result, the first model to create a first local model which relates to the predefined task;communicate with at least one other edge device in a first heterogeneous group of edge devices, wherein the first heterogeneous group of edge devices includes at least a first edge device and a second edge device, and the first edge device collects and analyzes the first type of data and the second edge device collects and analyzes a different second type of data;determine membership of a second heterogeneous group of edge devices from the first heterogeneous group of edge devices, the second group of edge devices being a subset of the first heterogeneous group of edge devices;determine a leader edge device from the second heterogeneous group of edge devices;receive, from the leader edge device, a request for the first local model;transmit the first local model to the leader edge device;receive, from the leader edge device, a mixed model which relates to the predefined task, wherein the mixed model is created by the leader edge device performing a mix operation of a plurality of the first local model and at least one different respective local model; andreplace the first local model with the mixed model.