Xu-Ting He

Towards Robust Federated Learning: A Centroid-Based Approach to Jointly Mitigate Noisy Labels and Non-IID Data

In federated learning (FL), client data often suffer from the challenges of data distribution imbalance such as Non-IID and noisy labels. Crucially, these two issues are highly coupled and mutually exacerbating: Non-IID data complicates the identification of noisy labels, while noisy labels severely amplify local model...