Machine learning with model filtering and model mixing for edge devices in a heterogeneous environment
First Claim
1. An edge device comprising:
- a communicator configured to communicate with a plurality of edge devices;
a data collector configured to collect data;
a memory configured to store the data collected by the data collector; and
one or more processors;
wherein the edge device is configured to;
analyze, by the one or more processors, using a local model, the data collected by the data collector;
transmit, by the communicator, requests for local models to the plurality of edge devices;
receive, by the communicator, a first plurality of local models from the plurality of edge devices, each of the first plurality of local models being updated by each of the plurality of edge devices based on analysis result of data corrected by the each of the plurality of edge devices;
filter, by the one or more processors, the first plurality of local models by at least one of structure metadata, context metadata, and data distribution;
select, by the one or more processors, a second plurality of local models from the first plurality of local models based on a result of the filtering;
generate, by the one or more processors, a mixed model from the second plurality of local models; and
transmit, by the communicator, the mixed model to other edge devices.
1 Assignment
0 Petitions
Accused Products
Abstract
Machine learning with model filtering and model mixing for edge devices in a heterogeneous environment is disclosed. In an example embodiment, an edge device includes a communication module, a data collection device, a memory, a machine learning module, and a model mixing module. The edge device analyzes collected data with a model for a first task, outputs a result, and updates the model to create a local model. The edge device communicates with other edge devices in a heterogeneous group, transmits a request for local models to the heterogeneous group, and receives local models from the heterogeneous group. The edge device filters the local models by structure metadata, including second local models, which relate to a second task. The edge device performs a mix operation of the second local models to generate a mixed model which relates to the second task, and transmits the mixed model to the heterogeneous group.
22 Citations
20 Claims
-
1. An edge device comprising:
-
a communicator configured to communicate with a plurality of edge devices; a data collector configured to collect data; a memory configured to store the data collected by the data collector; and one or more processors; wherein the edge device is configured to; analyze, by the one or more processors, using a local model, the data collected by the data collector; transmit, by the communicator, requests for local models to the plurality of edge devices; receive, by the communicator, a first plurality of local models from the plurality of edge devices, each of the first plurality of local models being updated by each of the plurality of edge devices based on analysis result of data corrected by the each of the plurality of edge devices; filter, by the one or more processors, the first plurality of local models by at least one of structure metadata, context metadata, and data distribution; select, by the one or more processors, a second plurality of local models from the first plurality of local models based on a result of the filtering; generate, by the one or more processors, a mixed model from the second plurality of local models; and transmit, by the communicator, the mixed model to other edge devices. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
-
-
14. A method comprising:
-
transmitting, by a communicator in an edge device, requests for local models to a plurality of edge devices; receiving, by the communicator, a first plurality of local models from the plurality of edge devices, each of the first plurality of local models being updated by each of the plurality of edge devices based on analysis result of data collected by the each of the plurality of edge devices; filtering, by one or more processors in the edge device, the first plurality of local models by at least one of structure metadata, context metadata, and data distribution; selecting, by the one or more processors, a second plurality of local models from the first plurality of local models based on a result of the filtering, generating, by the one or more processors, a mixed model from the second plurality of local models; and transmitting, by the communicator, the mixed model to other edge devices.
-
-
15. An edge device comprising:
-
a memory; and one or more processors coupled to the memory and configured to; transmit requests for local models to other edge devices, receive first local models from the other edge devices, each of the first local models being updated by each of the other edge devices based on analysis result of data collected by the each of the other edge devices, select second local models by filtering the first local models based on at least one of structure metadata, context metadata, and data distribution, generate a mixed model from the second local models, and transmit the mixed model to the other edge devices. - View Dependent Claims (16, 17, 18, 19, 20)
-
Specification