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PRoVeFL is a novel federated learning framework that achieves privacy-preserving, Byzantine-robust, and verifiable aggregation using multi-key fully homomorphic encryption, offering up to 100× runtime improvement over prior works.
FoggyTrust is a hierarchical extension of FLTrust that localizes trust computation to fog nodes, improving robustness against Byzantine attacks in heterogeneous federated learning settings, achieving over 50% improvement on challenging attacks like Krum and Trim on CIFAR-10.