FOCUS: Dealing with Label Quality Disparity in Federated Learning

Understand

Ubiquitous systems with End-Edge-Cloud architecture are increasingly being used in healthcare applications.

• Federated Learning (FL) is highly useful for such applications, due to silo effect and privacy preserving.
• Existing FL approaches generally do not account for disparities in the quality of local data labels.
• However, the clients in ubiquitous systems tend to suffer from label noise due to varying skill-levels, biases or malicious tampering of the annotators.