Simulate collaborative ML scenarios, experiment multi-partner learning approaches and measure respective contributions of different datasets to model performance.
Until now, we have been saving only the global accuracy and loss of the models.
As the multipartner learning scenarios are getting more complex, it would be interesting to evaluate the performance of the federated models per class, for example by saving the confusion matrix in the results.
Until now, we have been saving only the global accuracy and loss of the models.
As the multipartner learning scenarios are getting more complex, it would be interesting to evaluate the performance of the federated models per class, for example by saving the confusion matrix in the results.