FeDa4Fair is a new library and benchmark for creating federated datasets with heterogeneous client-level biases to standardize evaluation of fairness methods in federated learning.
Mitigating bias in federated learning
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Formulates a game where competitors in collaborative learning are incentivized to manipulate updates, then proposes mechanisms that restore honest participation for mean estimation, convex SGD, and non-convex federated learning.
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FeDa4Fair: Client-Level Federated Datasets for Fairness Evaluation
FeDa4Fair is a new library and benchmark for creating federated datasets with heterogeneous client-level biases to standardize evaluation of fairness methods in federated learning.
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Incentivizing Honesty among Competitors in Collaborative Learning and Optimization
Formulates a game where competitors in collaborative learning are incentivized to manipulate updates, then proposes mechanisms that restore honest participation for mean estimation, convex SGD, and non-convex federated learning.