In this paper, we initiate the study of local model reconstruction attacks
for federated learning, where a honest-but-curious adversary eavesdrops the
messages exchanged between a targeted client and the server, and then
reconstructs the local/personalized model of the victim. The local model
reconstruction attack allows the adversary to trigger other classical attacks
in a more effective way, since the local model only depends on the client's
data and can leak more private information than the global model learned by the
server. Additionally, we propose a novel model-based attribute inference attack
in federated learning leveraging the local model reconstruction attack. We
provide an analytical lower-bound for this attribute inference attack.
Empirical results using real world datasets confirm that our local
reconstruction attack works well for both regression and classification tasks.
Moreover, we benchmark our novel attribute inference attack against the
state-of-the-art attacks in federated learning. Our attack results in higher
reconstruction accuracy especially when the clients' datasets are
heterogeneous. Our work provides a new angle for designing powerful and
explainable attacks to effectively quantify the privacy risk in FL.

本文探讨本地模型重构攻击对于联邦学习的影响，提出了一种基于模型的特征推理攻击，并在实验中验证了攻击手法的有效性。