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Found 7 papers, 0 papers with code
AGIC: Approximate Gradient Inversion Attack on Federated Learning
Recent reconstruction attacks apply a gradient inversion optimization on the gradient update of a single minibatch to reconstruct the private data used by clients during training.
Fabricated Flips: Poisoning Federated Learning without Data
Consequently, we design REFD, a defense specifically crafted to protect against data-free attacks.
MEGA: Model Stealing via Collaborative Generator-Substitute Networks
However, they are all based on competing generator-substitute networks and hence encounter training instability. In this paper we propose a data-free model stealing frame-work, MEGA, which is based on collaborative generator-substitute networks and only requires the target model toprovide label prediction for synthetic query examples.
Attacks and Defenses for Free-Riders in Multi-Discriminator GAN
To mitigate the model degradation, we propose a defense strategy against free-riders in MD-GAN, termed DFG.
Confident Data-free Model Stealing for Black-box Adversarial Attacks
Deep machine learning models are increasingly deployed in the wild, subject to adversarial attacks.
Is Shapley Value fair? Improving Client Selection for Mavericks in Federated Learning
Shapley Value is commonly adopted to measure and incentivize client participation in federated learning.
Improving Medical Short Text Classification with Semantic Expansion Using Word-Cluster Embedding
However, in electronic medical records (EMR), the texts containing sentences are shorter than that in general domain, which leads to the lack of semantic features and the ambiguity of semantic.
