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Found 4 papers, 4 papers with code
DPD-fVAE: Synthetic Data Generation Using Federated Variational Autoencoders With Differentially-Private Decoder
Federated learning (FL) is getting increased attention for processing sensitive, distributed datasets common to domains such as healthcare.
Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-Ray Data
We demonstrate that both model architectures are vulnerable to privacy violation by applying image reconstruction attacks to local model updates from individual clients.
Implicit Model Specialization through DAG-based Decentralized Federated Learning
Federated learning allows a group of distributed clients to train a common machine learning model on private data.
Poisoning Attacks with Generative Adversarial Nets
In this paper we introduce a novel generative model to craft systematic poisoning attacks against machine learning classifiers generating adversarial training examples, i. e. samples that look like genuine data points but that degrade the classifier's accuracy when used for training.
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