FedBB addresses inter-case, inter-class, and inter-client imbalances in federated learning via Positive Negative Balanced loss and Client Balanced Reweighting, outperforming baselines on X-ray and natural image datasets while using limited statistics for privacy.
Addressing fairness, bias and class imbalance in machine learning: the fbi-loss.arXiv preprint arXiv:2105.06345, 2021
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Multi-Level Analyzation of Imbalance to Resolve Non-IID-Ness in Federated Learning
FedBB addresses inter-case, inter-class, and inter-client imbalances in federated learning via Positive Negative Balanced loss and Client Balanced Reweighting, outperforming baselines on X-ray and natural image datasets while using limited statistics for privacy.