FedP3E shares noisy class prototypes across federated clients plus SMOTE augmentation, reporting 95.1 to 99.6% accuracy on N-BaIoT under non-IID splits, beating FedAvg and FedProx.
An adaptive federated learning scheme with differential privacy preserving,
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FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning
FedP3E shares noisy class prototypes across federated clients plus SMOTE augmentation, reporting 95.1 to 99.6% accuracy on N-BaIoT under non-IID splits, beating FedAvg and FedProx.