An edge-cloud framework screens vibration events onboard with a GMM and uses a federated 1D Attention U-Net for temporal segmentation to detect potholes while reducing data transmission.
Federated deep learning for anomaly detection in the internet of things
2 Pith papers cite this work. Polarity classification is still indexing.
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A federated learning framework with homomorphic encryption and dynamic agent selection detects anomalies in IIoT while preserving privacy and reducing communication bottlenecks.
citing papers explorer
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Edge-Cloud Collaborative Pothole Detection via Onboard Event Screening and Federated Temporal Segmentation
An edge-cloud framework screens vibration events onboard with a GMM and uses a federated 1D Attention U-Net for temporal segmentation to detect potholes while reducing data transmission.
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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.