September 19, 2021

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Information Stealing in Federated Learning Systems Based on Generative Adversarial Networks. (arXiv:2108.00701v1 [cs.LG])

An attack on deep learning systems where intelligent machines collaborate to
solve problems could cause a node in the network to make a mistake on a
critical judgment. At the same time, the security and privacy concerns of AI
have galvanized the attention of experts from multiple disciplines. In this
research, we successfully mounted adversarial attacks on a federated learning
(FL) environment using three different datasets. The attacks leveraged
generative adversarial networks (GANs) to affect the learning process and
strive to reconstruct the private data of users by learning hidden features
from shared local model parameters. The attack was target-oriented drawing data
with distinct class distribution from the CIFAR- 10, MNIST, and Fashion-MNIST
respectively. Moreover, by measuring the Euclidean distance between the real
data and the reconstructed adversarial samples, we evaluated the performance of
the adversary in the learning processes in various scenarios. At last, we
successfully reconstructed the real data of the victim from the shared global
model parameters with all the applied datasets.