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Found 5 papers, 2 papers with code
A Secure and Efficient Federated Learning Framework for NLP
In this work, we consider the problem of designing secure and efficient federated learning (FL) frameworks.
Privacy-Preserving XGBoost Inference
Although machine learning (ML) is widely used for predictive tasks, there are important scenarios in which ML cannot be used or at least cannot achieve its full potential.
SAPAG: A Self-Adaptive Privacy Attack From Gradients
Distributed learning such as federated learning or collaborative learning enables model training on decentralized data from users and only collects local gradients, where data is processed close to its sources for data privacy.
Private Hierarchical Clustering and Efficient Approximation
In collaborative learning, multiple parties contribute their datasets to jointly deduce global machine learning models for numerous predictive tasks.
NED: An Inter-Graph Node Metric Based On Edit Distance
The inter-graph node similarity is important in learning a new graph based on the knowledge of an existing graph (transfer learning on graphs) and has applications in biological, communication, and social networks.
