Systems and methods for cooperative machine learning

Abstract

In some embodiments, a given client computing platform may include a client-side machine learning model configured to facilitate deep neural network operations on structured data. The operations may be performed by a client application installed on the given client computing platform. The client application may locally access the client-side machine learning model in order to perform the operations. Deep neural network operations on structured data may be performed at one or more servers. Sharing of model states may be facilitated between the cloud machine learning model and the client-side machine learning model. The cloud machine learning model may be improved, at one or more servers, based on usage of the application and user interactions with the given client computing platform.

What is claimed is: 
     
         1 . One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors, cause operations comprising:
 identifying a client-side machine learning model hosted on a user computing device, the client-side machine learning model being trained based on a training dataset;   obtaining model parameters of another machine learning model hosted on another computing device, the other machine learning model being trained at the other computing device based on another training dataset, the model parameters of the other machine learning model being determined via the training of the other machine learning model at the other computing device; and   when the user computing device is online, updating the client-side machine learning model based on the model parameters of the other machine learning model.   
     
     
         2 . The media of  claim 1 , the operations further comprising:
 obtaining, from the user computing device, model parameters of the client-side machine learning model, the model parameters of the client-side machine learning model being determined via the training of the client-side machine learning model at the user computing device; and   providing, to the other computing device, the model parameters of the client-side machine learning model to update the other machine learning model based on the model parameters of the client-side machine learning model, the model parameters of the client-side machine learning model indicating a model state of the client-side machine learning model.   
     
     
         3 . The media of  claim 1 , the operations further comprising:
 accessing the client-side machine learning model while the client-side machine learning model is hosted on the user computing device; and   obtaining the training dataset and providing the training dataset as input for the client-side machine learning model to train the client-side machine learning model, the client-side machine learning model being trained based on the training dataset when the user computing device is offline from a computer system that comprises the one or more processors.   
     
     
         4 . The media of  claim 1 , wherein the user computing device is a physical computing device remote from a computer system that comprises the one or more processors. 
     
     
         5 . The media of  claim 1 , wherein the user computing device is a mobile computing device. 
     
     
         6 . The media of  claim 1 , wherein the user computing device and the other computing device are physical computing devices remote from a computer system that comprises the one or more processors. 
     
     
         7 . The media of  claim 1 , wherein the user computing device and the other computing device are mobile computing devices. 
     
     
         8 . The media of  claim 1 , wherein the training dataset and the other training dataset comprise images, symbols, logos, videos, audio, text, geolocation, accelerometer data, or metadata. 
     
     
         9 . A method comprising:
 identifying, by one or more processors, a client-side machine learning model hosted on a user computing device, the client-side machine learning model being trained based on a training dataset;   obtaining, by the one or more processors, model parameters of another machine learning model hosted on another computing device, the other machine learning model being trained at the other computing device based on another training dataset, the model parameters of the other machine learning model being determined via the training of the other machine learning model at the other computing device; and   updating, by the one or more processors, the client-side machine learning model based on the model parameters of the other machine learning model.   
     
     
         10 . The method of  claim 9 , further comprising:
 obtaining, from the user computing device, model parameters of the client-side machine learning model, the model parameters of the client-side machine learning model being determined via the training of the client-side machine learning model at the user computing device; and   providing, to the other computing device, the model parameters of the client-side machine learning model to update the other machine learning model based on the model parameters of the client-side machine learning model, the model parameters of the client-side machine learning model indicating a model state of the client-side machine learning model.   
     
     
         11 . The method of  claim 9 , further comprising:
 accessing the client-side machine learning model while the client-side machine learning model is hosted on the user computing device; and   obtaining the training dataset and providing the training dataset as input for the client-side machine learning model to train the client-side machine learning model, the client-side machine learning model being trained based on the training dataset when the user computing device is offline from a computer system that comprises the one or more processors.   
     
     
         12 . The method of  claim 9 , wherein the user computing device is a physical computing device remote from a computer system that comprises the one or more processors. 
     
     
         13 . The method of  claim 9 , wherein the user computing device is a mobile computing device. 
     
     
         14 . The method of  claim 9 , wherein the user computing device and the other computing device are physical computing devices remote from a computer system that comprises the one or more processors. 
     
     
         15 . The method of  claim 9 , wherein the user computing device and the other computing device are mobile computing devices. 
     
     
         16 . The method of  claim 9 , wherein the training dataset and the other training dataset comprise images, symbols, logos, videos, audio, text, geolocation, accelerometer data, or metadata. 
     
     
         17 . A computer system comprising one or more processors programmed with instructions that, when executed by the one or more processors, cause operations comprising:
 identifying a client-side machine learning model hosted on a user computing device, the client-side machine learning model being trained based on a training dataset;   obtaining model parameters of another machine learning model hosted on another computing device, the other machine learning model being trained at the other computing device based on another training dataset, the model parameters of the other machine learning model being determined via the training of the other machine learning model at the other computing device; and   updating the client-side machine learning model based on the model parameters of the other machine learning model.   
     
     
         18 . The computer system of  claim 17 , the operations further comprising:
 obtaining, from the user computing device, model parameters of the client-side machine learning model, the model parameters of the client-side machine learning model being determined via the training of the client-side machine learning model at the user computing device; and   providing, to the other computing device, the model parameters of the client-side machine learning model to update the other machine learning model based on the model parameters of the client-side machine learning model, the model parameters of the client-side machine learning model indicating a model state of the client-side machine learning model.   
     
     
         19 . The computer system of  claim 17 , the operations further comprising:
 accessing the client-side machine learning model while the client-side machine learning model is hosted on the user computing device; and   obtaining the training dataset and providing the training dataset as input for the client-side machine learning model to train the client-side machine learning model, the client-side machine learning model being trained based on the training dataset when the user computing device is offline from the computer system.   
     
     
         20 . The computer system of  claim 17 , wherein the user computing device is a physical computing device remote from the computer system.