Training federated learning models

A computer system for training a federated learning model, the computer system comprising:

• one or more computer processors;
• one or more computer readable storage media;
• program instructions stored on the one or more computer readable storage media for execution by at least one of the one or more computer processors, the program instructions comprising instructions to:
• distribute a federated learning model to a plurality of computing nodes, wherein each computing node includes a set of local training data for training the federated learning model comprising data samples each labeled with one label of a plurality of labels;
• receive statistical data from each of the plurality of computing nodes, wherein the statistical data for a computing node is based on the local training data of the computing node, and wherein the statistical data indicates a count of data samples for each label;
• analyze the statistical data to identify one or more computing nodes having local training data in which a ratio of a count of data samples for one or more labels to a count of data samples for a most-represented label in the local training data is below a threshold value;
• provide additional training data to the identified one or more computing nodes, wherein the additional training data comprises data samples labeled with labels other than the most-represented label, and wherein the identified one or more computing nodes train the federated learning model using the set of local training data and the additional training data;
• and
• receive results from the plurality of computing nodes in response to the plurality of computing nodes each locally training the federated learning model, and generating a trained global model based on the results.

A computer program product for training a federated learning model, the computer program product comprising one or more computer readable storage media collectively having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to:

• distribute a federated learning model to a plurality of computing nodes, wherein each computing node includes a set of local training data for training the federated learning model comprising data samples each labeled with one label of a plurality of labels;
• receive statistical data from each of the plurality of computing nodes, wherein the statistical data for a computing node is based on the local training data of the computing node, and wherein the statistical data indicates a count of data samples for each label;
• analyze the statistical data to identify one or more computing nodes having local training data in which a ratio of a count of data samples for one or more labels to a count of data samples for a most-represented label in the local training data is below a threshold value;
• provide additional training data to the identified one or more computing nodes, wherein the additional training data comprises data samples labeled with labels other than the most-represented label, and wherein the identified one or more computing nodes train the federated learning model using the set of local training data and the additional training data;
• and
• receive results from the plurality of computing nodes in response to the plurality of computing nodes each locally training the federated learning model, and generating a trained global model based on the results.