  Federated learning (FL) empowers privacypreservation in model training by
only exposing users' model gradients. Yet, FL users are susceptible to gradient
inversion attacks (GIAs) which can reconstruct ground-truth training data such
as images based on model gradients. However, reconstructing high-resolution
images by existing GIAs faces two challenges: inferior accuracy and
slow-convergence, especially when duplicating labels exist in the training
batch. To address these challenges, we present an Accurate and Fast-convergent
Gradient Inversion attack algorithm, called AFGI, with two components: Label
Recovery Block (LRB) which can accurately restore duplicating labels of private
images based on exposed gradients; VME Regularization Term, which includes the
total variance of reconstructed images, the discrepancy between three-channel
means and edges, between values from exposed gradients and reconstructed
images, respectively. The AFGI can be regarded as a white-box attack strategy
to reconstruct images by leveraging labels recovered by LRB. In particular,
AFGI is efficient that accurately reconstruct ground-truth images when users'
training batch size is up to 48. Our experimental results manifest that AFGI
can diminish 85% time costs while achieving superb inversion quality in the
ImageNet dataset. At last, our study unveils the shortcomings of FL in
privacy-preservation, prompting the development of more advanced countermeasure
strategies.
