Distributed learning model for fog computing

Abstract

The disclosed technology relates to a process for metered training of fog nodes within the fog layer. The metered training allows the fog nodes to be continually trained within the fog layer without the need for the cloud. Furthermore, the metered training allows the fog node to operate normally as the training is performed only when spare resources are available at the fog node. The disclosed technology also relates to a process of sharing better trained machine learning models of a fog node with other similar fog nodes thereby speeding up the training process for other fog nodes within the fog layer.

1 . A method comprising:
 receiving, at an edge node, a machine learning model from a cloud, wherein the cloud provides the machine learning model with its initial training, the machine learning model being used to optimize performance of at least the edge node;   monitoring resources being used at the edge node, wherein a threshold amount of resources is identified as being an amount of resources needed for normal operations of the edge node;   identifying an amount of spare resources that are available at the edge node, wherein the amount of spare resources corresponds to a difference between a current amount of resources being used at the edge node that is less than the amount of resources needed for normal operations of the edge node;   allocating the identified amount of spare resources for metered training the machine learning model of the edge node, wherein the metered training prioritizes the normal operation of the edge node over training the machine learning model; and   training the machine learning model of the edge node using the identified amount of spare resources.   
     
     
         2 . The method of  claim 1 , wherein the monitoring is performed by a system performance monitor that is installed on the edge node. 
     
     
         3 . The method of  claim 1 , wherein the training of the machine learning model is performed using a sampled data set. 
     
     
         4 . The method of  claim 3 , wherein the sampled data set is based on the amount of spare resources available to the edge node. 
     
     
         5 . The method of  claim 1 , further comprising:
 automatically terminating a current training session of the machine learning model when the spare resources are no longer available at the edge node.   
     
     
         6 . The method of  claim 5 , further comprising:
 automatically terminating future training sessions and completing current training sessions of the machine learning model when the identified amount of spare resources available at the edge node falls below a pre-determined threshold amount.   
     
     
         7 . The method of  claim 1 , wherein the training of the machine learning model is performed so long as a pre-determined threshold amount of spare resources are available at the edge node. 
     
     
         8 . The method of  claim 1 , wherein the initial training of the machine learning model uses default values or past machine learning models of similar edge nodes stored within the cloud. 
     
     
         9 . The method of  claim 1 , wherein after being trained, the machine learning model is shared with at least one other edge node having a threshold similarity to the edge node. 
     
     
         10 . The method of  claim 9 , wherein the threshold similarity is based on at least a proximity of the edge node and the at least one other edge node. 
     
     
         11 . A non-transitory computer-readable medium comprising instructions which when executed by one or more processors at an edge node of a network, cause the edge node to:
 receive a machine learning model from a cloud, wherein the cloud provides the machine learning model with its initial training, the machine learning model being used to optimize performance of at least the edge node;   monitor resources being used at the edge node, wherein a threshold amount of resources is identified as being an amount of resources needed for normal operations of the edge node;   identify an amount of spare resources that are available at the edge node, wherein the amount of spare resources corresponds to a difference between a current amount of resources being used at the edge node that is less than the amount of resources needed for normal operations of the edge node;   allocate the identified amount of spare resources for metered training the machine learning model of the edge node, wherein the metered training prioritizes the normal operation of the edge node over training the machine learning model; and   train the machine learning model of the edge node using the identified amount of spare resources.   
     
     
         12 . The non-transitory computer-readable medium of  claim 11 , wherein the execution of the computer-readable instructions further cause the edge node to control a sampling of data being used to train the machine learning model. 
     
     
         13 . The non-transitory computer-readable medium of  claim 12 , wherein the sampling of data is based on the amount of spare resources available at the edge node. 
     
     
         14 . The non-transitory computer-readable medium of  claim 11 , wherein after being trained, the machine learning model is shared with at least one other edge node having a threshold similarity to the edge node. 
     
     
         15 . The non-transitory computer-readable medium of  claim 14 , wherein the threshold similarity is based on at least a proximity of the edge node and the at least one other edge node. 
     
     
         16 . An edge node of a network comprising:
 a processor; and   a non-transitory computer-readable medium storing instructions that, when executed by the system, cause the system to:
 receive a machine learning model from a cloud, wherein the cloud provides the machine learning model with its initial training, the machine learning model being used to optimize performance of at least the edge node; 
 monitor resources being used at the edge node, wherein a threshold amount of resources is identified as being an amount of resources needed for normal operations of the edge node; 
 identify an amount of spare resources that are available at the edge node, wherein the amount of spare resources corresponds to a difference between a current amount of resources being used at the edge node that is less than the amount of resources needed for normal operations of the edge node; 
 allocate the identified amount of spare resources for metered training the machine learning model of the edge node, wherein the metered training prioritizes the normal operation of the edge node over training the machine learning model; and 
 train the machine learning model of the edge node using the identified amount of spare resources. 
   
     
     
         17 . The system of an edge node of a network of  claim 16 , wherein the edge node is further configured to control a sampling of data being used to train the machine learning model. 
     
     
         18 . The system of an edge node of a network of  claim 16 , wherein the edge node is further configured to monitor the edge node to identify when spare resources are no longer available. 
     
     
         19 . The system of an edge node of a network of  claim 16 , wherein after being trained, the machine learning model is shared with at least one other edge node having a threshold similarity to the edge node. 
     
     
         20 . The system of an edge node of a network of  claim 19 , wherein the threshold similarity is based on at least a proximity of the edge node and the at least one other edge node.