MGIC: A Multi-Label Gradient Inversion Attack based on Canny Edge
  Detection on Federated Learning
Can Liu Jin Wang
As a new distributed computing framework that can protect data privacy,
federated learning (FL) has attracted more and more attention in recent years.
It receives gradients from users to train the global model and releases the
trained global model to working users. Nonetheless, the gradient inversion (GI)
attack reflects the risk of privacy leakage in federated learning. Attackers
only need to use gradients through hundreds of thousands of simple iterations
to obtain relatively accurate private data stored on users' local devices. For
this, some works propose simple but effective strategies to obtain user data
under a single-label dataset. However, these strategies induce a satisfactory
visual effect of the inversion image at the expense of higher time costs. Due
to the semantic limitation of a single label, the image obtained by gradient
inversion may have semantic errors. We present a novel gradient inversion
strategy based on canny edge detection (MGIC) in both the multi-label and
single-label datasets. To reduce semantic errors caused by a single label, we
add new convolution layers' blocks in the trained model to obtain the image's
multi-label. Through multi-label representation, serious semantic errors in
inversion images are reduced. Then, we analyze the impact of parameters on the
difficulty of input image reconstruction and discuss how image multi-subjects
affect the inversion performance. Our proposed strategy has better visual
inversion image results than the most widely used ones, saving more than 78% of
time costs in the ImageNet dataset.