Apparatus and method for hyperparameter optimization of a machine learning model in a federated learning system

A server apparatus comprising:

• a processor;
• and
• a memory having processor-executable instructions stored thereon, which when executed by the processor, case the server apparatus to:
• receive a set of current hyper-parameter values for a master machine learning model and corresponding validation set performance metrics from a federated learning server;
• determine an updated set of hyper-parameter values for the master machine learning model from the received set of hyper-parameter values and the corresponding validation set performance metrics;
• and
• send the updated set of hyper-parameter values for the master machine learning model to the federated learning server.

A method applied to a server apparatus, the method comprising:

• aggregating a plurality of received model updates to update a master machine learning model;
• determining whether a pre-defined threshold for received model updates is reached;
• transmitting a set of current hyper-parameter values and corresponding validation set performance metrics obtained from the updated master machine learning model to a hyper-parameter optimization model;
• receiving an updated set of hyper-parameter values from the hyper-parameter optimization model;
• updating the master machine learning model with the updated set of hyper-parameter values;
• and
• redistributing the updated master machine learning model with the updated set of hyper-parameter values to a plurality of clients.