  The main premise of federated learning is that machine learning model updates
are computed locally, in particular to preserve user data privacy, as those
never leave the perimeter of their device. This mechanism supposes the general
model, once aggregated, to be broadcast to collaborating and non malicious
nodes. However, without proper defenses, compromised clients can easily probe
the model inside their local memory in search of adversarial examples. For
instance, considering image-based applications, adversarial examples consist of
imperceptibly perturbed images (to the human eye) misclassified by the local
model, which can be later presented to a victim node's counterpart model to
replicate the attack. To mitigate such malicious probing, we introduce Pelta, a
novel shielding mechanism leveraging trusted hardware. By harnessing the
capabilities of Trusted Execution Environments (TEEs), Pelta masks part of the
back-propagation chain rule, otherwise typically exploited by attackers for the
design of malicious samples. We evaluate Pelta on a state of the art ensemble
model and demonstrate its effectiveness against the Self Attention Gradient
adversarial Attack.
