Data-Agnostic Model Poisoning against Federated Learning: A Graph Autoencoder Approach
This poses a serious threat to FL systems by enabling undetectable attacks that can infect all devices, though it is incremental as it builds on prior poisoning methods.
The paper tackles the problem of model poisoning attacks in Federated Learning by proposing a data-agnostic method using a graph autoencoder framework, which reduces FL accuracy gradually and evades detection by existing defenses.
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.