Search Results for author:
Found 4 papers, 1 papers with code
FedMM: Saddle Point Optimization for Federated Adversarial Domain Adaptation
Federated adversary domain adaptation is a unique distributed minimax training task due to the prevalence of label imbalance among clients, with each client only seeing a subset of the classes of labels required to train a global model.
A Stochastic Gradient Langevin Dynamics Algorithm For Noise Intrinsic Federated Learning
Our introduced SGLD oracle would lower generalization errors in local node's parameter learning and provide local node DP protections.
ASFGNN: Automated Separated-Federated Graph Neural Network
Specifically, to solve the data Non-IID problem, we first propose a separated-federated GNN learning model, which decouples the training of GNN into two parts: the message passing part that is done by clients separately, and the loss computing part that is learnt by clients federally.
Large-Scale Secure XGB for Vertical Federated Learning
Privacy-preserving machine learning has drawn increasingly attention recently, especially with kinds of privacy regulations come into force.
