Reward Systems for Trustworthy Medical Federated Learning

Understand

Federated learning (FL) has received high interest from researchers and practitioners to train machine learning (ML) models for healthcare.

• Ensuring the trustworthiness of these models is essential.
• Especially bias, defined as a disparity in the model's predictive performance across different subgroups, may cause unfairness against specific subgroups, which is an undesired phenomenon for trustworthy ML models.
• In this research, we address the question to which extent bias occurs in medical FL and how to prevent excessive bias through reward systems.