METHODS AND APPARATUS FOR A STATISTICALLY OPTIMIZED LEARNING FRAMEWORK OFFERING BIAS MITIGATION

An example apparatus disclosed includes interface circuitry, machine readable instructions, and programmable circuitry to at least one of execute or instantiate the machine readable instructions to send a global model to one or more collaborator models, the one or more collaborator models training on a local dataset associated with a collaborator model, receive one or more collaborator models trained on the local dataset, compute a similarity measurement between the global model and at least one collaborator model, determine aggregation for the global model based on the computed similarity measurement, aggregate one or more one or more collaborator models based on the determined aggregation, and update the global model based on the aggregation.