Managing federated learning performed on multiple endpoint devices

Abstract

Aspects of the present disclosure are directed to a federated learning platform that coordinates applications configured to perform federated learning on client devices in a manner that controls and manages resource usage. For example, the platform can allocate time to applications configured to perform federated learning, such as based on application priority orders, device capabilities and statuses, available time to perform federated learning, etc. The platform can further apply mechanisms to determine which applications are performing federated learning to enforce the coordination, and allow applications to share training time, training data, or machine learning models that produce similar results. In some implementations, the federated learning platform can provide controls to users to understand and limit the federated learning that is being performed on the devices, such as via a user interface dashboard.