Abstract
Attacks on Federated Learning (FL) can severely reduce the quality of the generated models and limit the usefulness of this emerging learning paradigm that enables on-premise decentralized learning. There have been various untargeted attacks on FL, but they are not widely applicable as they i) assume that the attacker knows every update of benign clients, which is indeed sent in encrypted form to the central server, or ii) assume that the attacker has a large dataset and sufficient resources to locally train updates imitating benign parties. In this paper, we design a zero-knowledge untargeted attack (ZKA), which synthesizes malicious data to craft adversarial models without eavesdropping on the transmission of benign clients at all or requiring a large quantity of task-specific training data. To inject malicious input into the FL system by synthetic data, ZKA has two variants. ZKA-R generates adversarial ambiguous data by reversing engineering from the global models. To enable stealthiness, ZKA-G trains the local model on synthetic data from the generator that aims to synthesize images different from a randomly chosen class. Furthermore, we add a novel distance-based regularization term for both attacks to further enhance stealthiness. Experimental results on Fashion-MNIST and CIFAR-10 show that the ZKA achieves similar or even higher attack success rate than the state-of-the-art untargeted attacks against various defense mechanisms, namely more than 50% for Cifar-10 for all considered defense mechanisms. As expected, ZKA-G is better at circumventing defenses, even showing a defense pass rate of close to 90% when ZKA-R only achieves 70%. Higher data heterogeneity favours ZKA-R since detection becomes harder.
Abstract (translated)
URL
https://arxiv.org/abs/2202.05877
