Maximizing Global Model Appeal in Federated Learning

Understand

Federated learning typically considers collaboratively training a global model using local data at edge clients.

• Clients may have their own individual requirements, such as having a minimal training loss threshold, which they expect to be met by the global model.
• However, due to client heterogeneity, the global model may not meet each client's requirements, and only a small subset may find the global model appealing.
• In this work, we explore the problem of the global model lacking appeal to the clients due to not being able to satisfy local requirements.