Multi-Level Analyzation of Imbalance to Resolve Non-IID-Ness in Federated Learning

The paper introduces FedBB, a novel approach to address class imbalance in federated learning (FL) by analyzing imbalance issues at three levels: inter-case, inter-class, and inter-client. FedBB employs a Positive Negative Balanced (PNB) loss function to enhance local training on skewed datasets and a Client Balanced Reweighting (CBR) mechanism to adjust model aggregation based on client data distribution. Experimental results on X-ray and natural image datasets show that FedBB outperforms existing methods, making it a valuable framework for improving model generalization in non-IID scenarios while maintaining privacy through minimal statistical data requirements.