Artur Sousa Freitas

Improving Federated Learning under Non-IID Data through Clustered Ensembles and Adaptive Client Selection

Federated Learning (FL) enables collaborative model training without sharing raw data, but its effectiveness degrades under Non-IID client data and inefficient participation. In such settings, FedAvg may exhibit slow convergence, high variance across rounds, and elevated communication overhead. We propose a clustered e...