ARES: Scalable and Practical Gradient Inversion Attack in Federated Learning through Activation Recovery
ARES: Scalable and Practical Gradient Inversion Attack in Federated Learning through Activation Recovery explores ARES is a novel gradient inversion attack that reconstructs training samples in federated learning without architectural modifications.. Commercial viability score: 7/10 in Federated Learning Security.
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