Data-Agnostic Model Poisoning against Federated Learning: A Graph
  Autoencoder Approach
Kai Li Jingjing Zheng Xin Yuan Wei Ni Ozgur B. Akan H. Vincent Poor
This paper proposes a novel, data-agnostic, model poisoning attack on
Federated Learning (FL), by designing a new adversarial graph autoencoder
(GAE)-based framework. The attack requires no knowledge of FL training data and
achieves both effectiveness and undetectability. By listening to the benign
local models and the global model, the attacker extracts the graph structural
correlations among the benign local models and the training data features
substantiating the models. The attacker then adversarially regenerates the
graph structural correlations while maximizing the FL training loss, and
subsequently generates malicious local models using the adversarial graph
structure and the training data features of the benign ones. A new algorithm is
designed to iteratively train the malicious local models using GAE and
sub-gradient descent. The convergence of FL under attack is rigorously proved,
with a considerably large optimality gap. Experiments show that the FL accuracy
drops gradually under the proposed attack and existing defense mechanisms fail
to detect it. The attack can give rise to an infection across all benign
devices, making it a serious threat to FL.