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Found 3 papers, 1 papers with code
FedQV: Leveraging Quadratic Voting in Federated Learning
Federated Learning (FL) permits different parties to collaboratively train a global model without disclosing their respective local labels.
PriPrune: Quantifying and Preserving Privacy in Pruned Federated Learning
Federated learning (FL) is a paradigm that allows several client devices and a server to collaboratively train a global model, by exchanging only model updates, without the devices sharing their local training data.
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.
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