UDJ-FL is a single federated learning objective whose hyperparameters select among four distributive-justice fairness notions, with client weights set by aleatoric uncertainty.
E2FL: Equal and Equitable Federated Learning
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abstract
Federated Learning (FL) enables data owners to train a shared global model without sharing their private data. Unfortunately, FL is susceptible to an intrinsic fairness issue: due to heterogeneity in clients' data distributions, the final trained model can give disproportionate advantages across the participating clients. In this work, we present Equal and Equitable Federated Learning (E2FL) to produce fair federated learning models by preserving two main fairness properties, equity and equality, concurrently. We validate the efficiency and fairness of E2FL in different real-world FL applications, and show that E2FL outperforms existing baselines in terms of the resulting efficiency, fairness of different groups, and fairness among all individual clients.
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cs.LG 1years
2025 1verdicts
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Achieving Distributive Justice in Federated Learning via Uncertainty Quantification
UDJ-FL is a single federated learning objective whose hyperparameters select among four distributive-justice fairness notions, with client weights set by aleatoric uncertainty.