The work provides the first formal definitions of Rashomon sets for federated learning and introduces a multiplicity-aware training pipeline evaluated on standard benchmarks.
Bias propagation in federated learning
4 Pith papers cite this work. Polarity classification is still indexing.
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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.
Moral judgments become more deontological when human design of AI is visible, and designers are judged more strictly than the AI or unaided humans, creating plural and non-converging targets for value alignment.
Proactive client selection in federated learning via differentially private mutual information and simulated annealing to optimize Potential Federation Loss for utility and fairness.
citing papers explorer
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Rashomon Sets and Model Multiplicity in Federated Learning
The work provides the first formal definitions of Rashomon sets for federated learning and introduces a multiplicity-aware training pipeline evaluated on standard benchmarks.
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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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The Alignment Target Problem: Divergent Moral Judgments of Humans, AI Systems, and Their Designers
Moral judgments become more deontological when human design of AI is visible, and designers are judged more strictly than the AI or unaided humans, creating plural and non-converging targets for value alignment.
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Choose Wisely and Privately: Proactive Client Selection for Fair and Efficient Federated Learning
Proactive client selection in federated learning via differentially private mutual information and simulated annealing to optimize Potential Federation Loss for utility and fairness.