DRAGD and DRAGDP reconstruct erased federated-learning images by sequentially matching post-unlearning and pre-unlearning gradients, with DRAGDP adding a public-image prior.
Exploiting subtle differences in gradients before and after data removal, the DLG attack reconstructs sensitive data points by comparing gradients from the gl obal model
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DRAGD: A Federated Unlearning Data Reconstruction Attack Based on Gradient Differences
DRAGD and DRAGDP reconstruct erased federated-learning images by sequentially matching post-unlearning and pre-unlearning gradients, with DRAGDP adding a public-image prior.