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GI-SMN: Gradient Inversion Attack against Federated Learning without Prior Knowledge
Recent work has demonstrated that gradient inversion attacks can exploit the gradients of FL to recreate the original user data, posing significant privacy risks.
Distributional Black-Box Model Inversion Attack with Multi-Agent Reinforcement Learning
Then, it randomly chooses latent codes from the latent probability distribution for recovering the private data.
SR-HetGNN:Session-based Recommendation with Heterogeneous Graph Neural Network
The current studies generally learn user preferences according to the transitions of items in the user's session sequence.
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