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Found 5 papers, 1 papers with code
Aergia: Leveraging Heterogeneity in Federated Learning Systems
Federated Learning (FL) is a popular approach for distributed deep learning that prevents the pooling of large amounts of data in a central server.
MUDGUARD: Taming Malicious Majorities in Federated Learning using Privacy-Preserving Byzantine-Robust Clustering
It is therefore currently impossible to ensure Byzantine robustness and confidentiality of updates without assuming a semi-honest majority.
I-GWAS: Privacy-Preserving Interdependent Genome-Wide Association Studies
We introduce I-GWAS, a novel framework that securely computes and releases the results of multiple possibly interdependent GWASes.
AGIC: Approximate Gradient Inversion Attack on Federated Learning
Recent reconstruction attacks apply a gradient inversion optimization on the gradient update of a single minibatch to reconstruct the private data used by clients during training.
Federated Geometric Monte Carlo Clustering to Counter Non-IID Datasets
Federated learning allows clients to collaboratively train models on datasets that are acquired in different locations and that cannot be exchanged because of their size or regulations.
