A single adversary in distributed training inflates its attribution value via latent optimization on synthetic batches without degrading accuracy or triggering basic defenses.
Scaling laws for the value of individual data points in machine learning
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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.