Yuan-Heng Tsai

Assessing the Impacts of Imperfect Datasets on Client Selections in Federated Learning

This study experimentally measures the impact of non-IID data, noisy data, and fairness in client selection on model accuracy and convergence, and proposes a privacy-preserving scoring method to assess each client's contribution in FL.