This page explains how we can help you to improve your model performance beyond federated learning
We understand that improving performance in a federated learning scenario requires collaboration. There can be instances where you would need further help like:
Getting second set of annotations on client data
Extracting weak labels from reports
Viewing actual data
Implementing custom aggregation methods
Implementing communication/networking algorithms.
We can provide you support in all of the above scenarios on a case-to-case basis.