  Gradient Inversion Attacks invert the transmitted gradients in Federated
Learning (FL) systems to reconstruct the sensitive data of local clients and
have raised considerable privacy concerns. A majority of gradient inversion
methods rely heavily on explicit prior knowledge (e.g., a well pre-trained
generative model), which is often unavailable in realistic scenarios. To
alleviate this issue, researchers have proposed to leverage the implicit prior
knowledge of an over-parameterized network. However, they only utilize a fixed
neural architecture for all the attack settings. This would hinder the adaptive
use of implicit architectural priors and consequently limit the
generalizability. In this paper, we further exploit such implicit prior
knowledge by proposing Gradient Inversion via Neural Architecture Search
(GI-NAS), which adaptively searches the network and captures the implicit
priors behind neural architectures. Extensive experiments verify that our
proposed GI-NAS can achieve superior attack performance compared to
state-of-the-art gradient inversion methods, even under more practical settings
with high-resolution images, large-sized batches, and advanced defense
strategies.
