FedQual improves federated label distribution learning under heterogeneous annotation quality via quality-adaptive training with a global anchor and reliability-aware aggregation, backed by new benchmarks and a proof that client-specific calibration strictly outperforms uniform calibration.
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Trustworthy Federated Label Distribution Learning under Annotation Quality Disparity
FedQual improves federated label distribution learning under heterogeneous annotation quality via quality-adaptive training with a global anchor and reliability-aware aggregation, backed by new benchmarks and a proof that client-specific calibration strictly outperforms uniform calibration.