Analysing the Influence of Attack Configurations on the Reconstruction of Medical Images in Federated Learning. (arXiv:2204.13808v1 [eess.IV])

The idea of federated learning is to train deep neural network models
collaboratively and share them with multiple participants without exposing
their private training data to each other. This is highly attractive in the
medical domain due to patients' privacy records. However, a recently proposed
method called Deep Leakage from Gradients enables attackers to reconstruct data
from shared gradients. This study shows how easy it is to reconstruct images
for different data initialization schemes and distance measures. We show how …

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