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Found 5 papers, 0 papers with code
Survey of Privacy Threats and Countermeasures in Federated Learning
In this paper, we describe privacy threats and countermeasures for the typical types of federated learning; horizontal federated learning, vertical federated learning, and transfer federated learning.
Heterogeneous Domain Adaptation with Positive and Unlabeled Data
To overcome this issue, we propose a novel method, predictive adversarial domain adaptation (PADA), which can predict likely positive examples from the unlabeled target data and simultaneously align the feature spaces to reduce the distribution divergence between the whole source data and the likely positive target data.
Personalized Federated Learning with Multi-branch Architecture
We also design an aggregation method to improve the communication efficiency and the model performance, with which each branch is globally updated with weighted averaging by client-specific weights assigned to the branch.
Continual Horizontal Federated Learning for Heterogeneous Data
Federated learning is a promising machine learning technique that enables multiple clients to collaboratively build a model without revealing the raw data to each other.
Knowledge Cross-Distillation for Membership Privacy
A membership inference attack (MIA) poses privacy risks for the training data of a machine learning model.
