Search Results for author: Mao V. Ngo

Found 4 papers, 1 papers with code

Long-Short History of Gradients is All You Need: Detecting Malicious and Unreliable Clients in Federated Learning

1 code implementation14 Aug 2022 Ashish Gupta, Tie Luo, Mao V. Ngo, Sajal K. Das

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.

Federated Learning

Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach

no code implementations9 Aug 2021 Mao V. Ngo, Tie Luo, Tony Q. S. Quek

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.

Anomaly Detection Edge-computing +1

Adaptive Anomaly Detection for IoT Data in Hierarchical Edge Computing

no code implementations10 Jan 2020 Mao V. Ngo, Hakima Chaouchi, Tie Luo, Tony Q. S. Quek

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.

Anomaly Detection Edge-computing

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