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Found 3 papers, 1 papers with code
Mitigating Cross-client GANs-based Attack in Federated Learning
Since a benign client's data can be leaked to the adversary, this attack brings the risk of local data leakage for clients in many security-critical FL applications.
Handling Data Heterogeneity in Federated Learning via Knowledge Distillation and Fusion
The key idea in FedKF is to let the server return the global knowledge to be fused with the local knowledge in each training round so that the local model can be regularized towards the global optima.
Federated Learning with Domain Generalization
Federated Learning (FL) enables a group of clients to jointly train a machine learning model with the help of a centralized server.
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