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Found 7 papers, 0 papers with code
Learning with User-Level Local Differential Privacy
Under the central model, user-level DP is strictly stronger than the item-level one.
Enhancing Learning with Label Differential Privacy by Vector Approximation
Label differential privacy (DP) is a framework that protects the privacy of labels in training datasets, while the feature vectors are public.
CG-FedLLM: How to Compress Gradients in Federated Fune-tuning for Large Language Models
The success of current Large-Language Models (LLMs) hinges on extensive training data that is collected and stored centrally, called Centralized Learning (CL).
Differential Private Knowledge Transfer for Privacy-Preserving Cross-Domain Recommendation
To this end, PriCDR can not only protect the data privacy of the source domain, but also alleviate the data sparsity of the source domain.
Integrated routing for a vehicle-robot pickup and delivery system with time constraints
This paper considers an unmanned vehicle-robot pickup and delivery system, in which a self-driving vehicle carrying multiple unmanned robots in the form of the mother ship travels from a depot to a number of stations distributed in a neighborhood to perform multiple pickup and delivery services.
A Theoretical Perspective on Differentially Private Federated Multi-task Learning
In the era of big data, the need to expand the amount of data through data sharing to improve model performance has become increasingly compelling.
Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification
Recently, Graph Neural Network (GNN) has achieved remarkable progresses in various real-world tasks on graph data, consisting of node features and the adjacent information between different nodes.
