  Federated Learning (FL) enables collaborative training of Deep Learning (DL)
models where the data is retained locally. Like DL, FL has severe security
weaknesses that the attackers can exploit, e.g., model inversion and backdoor
attacks. Model inversion attacks reconstruct the data from the training
datasets, whereas backdoors misclassify only classes containing specific
properties, e.g., a pixel pattern. Backdoors are prominent in FL and aim to
poison every client model, while model inversion attacks can target even a
single client.
  This paper introduces a novel technique to allow backdoor attacks to be
client-targeted, compromising a single client while the rest remain unchanged.
The attack takes advantage of state-of-the-art model inversion and backdoor
attacks. Precisely, we leverage a Generative Adversarial Network to perform the
model inversion. Afterward, we shadow-train the FL network, in which, using a
Siamese Neural Network, we can identify, target, and backdoor the victim's
model. Our attack has been validated using the MNIST, F-MNIST, EMNIST, and
CIFAR-100 datasets under different settings -- achieving up to 99\% accuracy on
both source (clean) and target (backdoor) classes and against state-of-the-art
defenses, e.g., Neural Cleanse, opening a novel threat model to be considered
in the future.
