Gradient inversion-based inference attack against federated learning - Chan, Joel Yuan Wei (Nanyang Technological University, 2023)
Federated learning is a state-of-the-art paradigm where deep learning models based on servers can be trained without having direct access to private training data. In federated learning, clients transfer gradients to the server, which can be used to further improve the model. However, the gradients transferred are susceptible to leaking the private data to the server, and this is a concern in many real-life applications, such as medical image classification. This attack is called gradient inversion.