A federated learning framework with homomorphic encryption and dynamic agent selection detects anomalies in IIoT while preserving privacy and reducing communication bottlenecks.
A novel buffered federated learning framework for privacy-driven anomaly detection in iiot
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CR 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Towards Securing IIoT: An Innovative Privacy-Preserving Anomaly Detector Based on Federated Learning
A federated learning framework with homomorphic encryption and dynamic agent selection detects anomalies in IIoT while preserving privacy and reducing communication bottlenecks.