Model improvement using federated learning and canonical feature mapping

Abstract

The method provides for receiving a plurality of trained models from a corresponding plurality of clients, wherein a respective trained model predicts a condition of an asset and is based on a data set associated with the asset of a respective client. The trained model is based on a seed model that includes a canonical set of features. The trained model includes a component that converts the data at a site to the canonical set of features used by the seed model. The plurality of trained models from the corresponding plurality of clients is assigned to two or more groupings, wherein a grouping includes trained models providing similar analysis. The one or more processors generate an improved model for a client with a limited amount of training data, obtaining the improvement by using multiple models that belong to the same grouping of the first client's model.

What is claimed is: 
     
         1 . A computer program product for generating an artificial intelligence (AI) model, the computer program product comprising:
 at least one computer readable storage medium, and program instructions stored on the at least one computer readable storage medium, the program instructions comprising:
 program instructions to receive information associated with respective existing models from a plurality of clients; 
 program instructions to group the respective existing models from the plurality of clients into domains based on the received information of the respective existing models; 
 program instructions to send a seed model to a first set of clients of the plurality of clients that correspond to existing models that are grouped into a first domain; 
 program instructions to receive confirmation of mapping feature data of the respective existing models of the first set of clients to a canonical schema of the seed model; 
 program instructions to send a base model with the canonical schema associated with the seed model, respectively, to the first set of clients; 
 program instructions to receive from the first set of clients, respectively, the base model that is trained by the feature data of the respective existing models of the first set of clients; and 
 program instructions to generate an augmented model by federation of attributes from the received base models of the first set of clients, trained by the feature data of the respective existing models of the first set of clients. 
   
     
     
         2 . The computer program product of  claim 1 , further comprising:
 program instructions to send the augmented model to respective clients of the first set of clients, wherein a feature mapper of the respective clients of the first set of clients is prepended to the augmented model.   
     
     
         3 . The computer program product of  claim 1 , wherein the feature data of respective clients of the first set of clients is private data that is unshared, remains with a respective client, and is used by the respective client to generate a feature mapper with the seed model and to train the base model. 
     
     
         4 . The computer program product of  claim 1 , wherein program instructions to map the feature data of the existing model of a respective client to the canonical schema, further comprises:
 program instructions to communicate a procedure to generate an algorithm to translate respective features of the feature data of the existing model of the respective client to the canonical schema of the seed model; and   program instructions to communicate a procedure to apply the feature data of a respective client's asset to the algorithm transforming the feature data to an input feature of the canonical schema of the augmented model.   
     
     
         5 . The computer program product of  claim 1 , wherein program instructions to generate the augmented model further comprises:
 program instructions to perform learning federation techniques on the received trained base models from the respective clients; and   program instructions to generate a single augmented model including attributes of the base model that is trained and received, respectively, from the first set of clients.   
     
     
         6 . The computer program product of  claim 1 , wherein the canonical schema of the seed model and the base model include one or more input features and at least one output feature. 
     
     
         7 . The computer program product of  claim 1 , wherein a domain of the respective existing models includes models performing similar types of analysis. 
     
     
         8 . A computer system for improving a model based on augmenting a plurality of models trained on private feature data, the method comprising:
 one or more computer processors;   at least one computer readable storage medium; and   program instructions stored on the at least one computer readable storage medium, the program instructions comprising:
 program instructions to send an existing model of a first model type to a model augmentation service; 
 program instructions to receive a seed model that includes a canonical schema of data input and output; 
 program instructions to train the seed model by generating a feature mapper that maps private feature data of the existing model to the canonical schema of the seed model; 
 program instructions to send a confirmation of completion of mapping of the private feature data of the existing model to the canonical schema of the seed model to the model augmentation service; 
 program instructions to receive a base model including the canonical schema of the seed model; 
 program instructions to train the base model by applying the private feature data of the existing model to the canonical schema of inputs by use of the feature mapper; 
 program instructions to send the trained base model to a model augmentation service; 
 program instructions to receive from the model augmentation service, a single augmented model generated by application of federated learning applied to a plurality of base models of the first model type; and 
 program instructions to prepend the feature mapper to the single augmented base model received from the model augmentation service, wherein the private feature data of the existing model is applied to the feature mapper prepended to the single augmented model. 
   
     
     
         9 . The computer system of  claim 8 , further comprising:
 program instructions to communicate a technique of training the base model by applying the private feature data of the existing model to the canonical schema of the base model by use of the feature mapper.   
     
     
         10 . The computer system of  claim 8 , wherein a varying number of features of the private feature data of the existing model are mapped to a fixed number of canonical features of the seed model. 
     
     
         11 . The computer system of  claim 8 , wherein the seed model is used as the base model. 
     
     
         12 . The computer system of  claim 8 , wherein one or more base models of the plurality of base models used to generate the signal augmented model are from historically received base models having a similar first model type. 
     
     
         13 . A computer program product system for improving a model based on augmenting a plurality of models trained on private feature data, the method comprising:
 at least one computer readable storage medium and program instructions stored on the at least one computer readable storage medium, the program instructions comprising:
 program instructions to send an existing model of a first model type to a model augmentation service; 
 program instructions to receive a seed model that includes a canonical schema of data input and output; 
 program instructions to train the seed model by generating a feature mapper that maps private feature data of the existing model to the canonical schema of the seed model; 
 program instructions to send a confirmation of completion of mapping of the private feature data of the existing model to the canonical schema of the seed model to the model augmentation service; 
 program instructions to receive a base model including the canonical schema of the seed model; 
 program instructions to train the base model by applying the private feature data of the existing model to the canonical schema of inputs by use of the feature mapper; 
 program instructions to send the trained base model to a model augmentation service; 
 program instructions to receive from the model augmentation service, a single augmented model generated by application of federated learning applied to a plurality of base models of the first model type; and 
 program instructions to prepend the feature mapper to the single augmented base model received from the model augmentation service, wherein the private feature data of the existing model is applied to the feature mapper prepended to the single augmented model. 
   
     
     
         14 . The computer program product of  claim 13 , the stored program instructions further comprising program instructions to:
 communicate a technique of training the base model by applying the private feature data of the existing model to the canonical schema of the base model by use of the feature mapper.   
     
     
         15 . The computer program product of  claim 13 , further comprising program instructions, stored on the at least one computer readable storage medium to:
 generate the single augmented model by repeatedly averaging the wights of the plurality of base models of the first model type.   
     
     
         16 . The computer program product of  claim 13 , wherein the mapping of the feature data of the existing model of the respective client to the canonical schema further comprises program instructions, stored on the at least one computer readable storage medium, to:
 generate an algorithm to translate a feature of the private feature data of the existing model to the canonical schema of the seed model; and   apply the private feature data of the existing model to the algorithm transforming the a feature of the private feature of the existing model to an input feature of the canonical schema of the augmented model.   
     
     
         17 . The computer program product of  claim 13 , wherein program instructions to generate the augmented model further comprises program instructions, stored on the at least one computer readable storage medium for execution by at least one of the one or more processors to:
 generate the feature mapper based on the seed model received; and   train the base model using the feature mapper to map the private feature data of the existing model to the canonical schema inputs of the base model, wherein the private feature data is unshared.   
     
     
         18 . The computer program product of  claim 13 , wherein a varying number of features of the private feature data of the existing model are mapped to a fixed number of canonical features of the seed model. 
     
     
         19 . The computer program product of  claim 13 , wherein one or more base models of the plurality of base models used to generate the signal augmented model are from historically received base models having a similar first model type. 
     
     
         20 . The computer program product of  claim 13 , wherein a domain of the respective existing models includes models performing similar types of analysis.