K-SVFair-FBF uses the new K-Shapley value to achieve meritocratic fairness with O(T^{3/4}) regret in budgeted combinatorial bandits under full-bandit feedback.
Re- defining contributions: Shapley-driven federated learning
2 Pith papers cite this work. Polarity classification is still indexing.
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A single adversary in distributed training inflates its attribution value via latent optimization on synthetic batches without degrading accuracy or triggering basic defenses.
citing papers explorer
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Meritocratic Fairness in Budgeted Combinatorial Multi-armed Bandits via Shapley Values
K-SVFair-FBF uses the new K-Shapley value to achieve meritocratic fairness with O(T^{3/4}) regret in budgeted combinatorial bandits under full-bandit feedback.
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On the Fragility of Data Attribution When Learning Is Distributed
A single adversary in distributed training inflates its attribution value via latent optimization on synthetic batches without degrading accuracy or triggering basic defenses.