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.
Graph representation feder- ated learning for malware detection in internet of health things,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CR 1years
2025 1verdicts
REJECT 1representative citing papers
citing papers explorer
-
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.