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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.
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.
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.
End-to-End Learning from Noisy Crowd to Supervised Machine Learning Models
Furthermore, relabeling only 10% of the data using the expert's results in over 90% classification accuracy with SVM.
Online Label Aggregation: A Variational Bayesian Approach
Our evaluation results on various online scenarios show that BiLA can effectively infer the true labels, with an error rate reduction of at least 10 to 1. 5 percent points for synthetic and real-world datasets, respectively.
Generative Models for Learning from Crowds
In this paper, we propose generative probabilistic models for label aggregation.
