A two-stage framework that applies fairness post-processing locally on each federated learning client consistently reduces equalized odds gaps across tabular, ECG, and X-ray datasets.
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A Post-Processing-Based Fair Federated Learning Framework
A two-stage framework that applies fairness post-processing locally on each federated learning client consistently reduces equalized odds gaps across tabular, ECG, and X-ray datasets.