With the development of artificial intelligence technology, Federated Learning (FL) model
has been widely used in many industries for its high efficiency and confidentiality. Some …

Exchanging gradient is a widely used method in modern multinode machine learning
system (eg, distributed training, Federated Learning). Gradients and weights of model has …

Federated Learning (FL) improves the privacy of local training data by exchanging model
updates (eg, local gradients or updated parameters). Gradients and weights of the model …

In order to preserve data privacy while fully utilizing data from different owners, federated
learning is believed to be a promising approach in recent years. However, aiming at …

Federated learning protects the privacy information in the data set by sharing the average
gradient. However,“Deep Leakage from Gradient”(DLG) algorithm as a gradient-based …

Federated learning (FL) is a distributed machine learning technique that guarantees the
privacy of user data. However, FL has been shown to be vulnerable to gradient leakage …

Reconstruction attacks and defenses are essential in understanding the data leakage
problem in machine learning. However, prior work has centered around empirical …

Federated learning can complete the neural network model training without uploading users'
private data. However, the deep leakage from gradients (DLG) and the compensatory …

In the federated learning scenario, the private data are kept local, and gradients are shared
to train the global model. Because gradients are updated according to the private training …

Federated Learning (FL) is susceptible to gradient leakage attacks, as recent studies show
the feasibility of obtaining private training data on clients from publicly shared gradients …