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Dealing Doubt: Unveiling Threat Models in Gradient Inversion Attacks under Federated Learning, A Survey and Taxonomy
While existing surveys on GIAs have focused on the honest-but-curious server threat model, there is a dearth of research categorizing attacks under the realistic and far more privacy-infringing cases of malicious servers and clients.
CoDream: Exchanging dreams instead of models for federated aggregation with heterogeneous models
Federated Learning (FL) enables collaborative optimization of machine learning models across decentralized data by aggregating model parameters.
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