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Found 4 papers, 1 papers with code
Long-Short History of Gradients is All You Need: Detecting Malicious and Unreliable Clients in Federated Learning
Not only this, but we can also distinguish between targeted and untargeted attacks among malicious clients, unlike most prior works which only consider one type of the attacks.
Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach
In comparison with both baseline and state-of-the-art schemes, our adaptive approach strikes the best accuracy-delay tradeoff on the univariate dataset, and achieves the best accuracy and F1-score on the multivariate dataset with only negligibly longer delay than the best (but inflexible) scheme.
Contextual-Bandit Anomaly Detection for IoT Data in Distributed Hierarchical Edge Computing
We build an HEC testbed, implement our proposed approach, and evaluate it using real IoT datasets.
Adaptive Anomaly Detection for IoT Data in Hierarchical Edge Computing
We evaluate our proposed approach using a real IoT dataset, and demonstrate that it reduces detection delay by 84% while maintaining almost the same accuracy as compared to offloading detection tasks to the cloud.
