3 citations · 5 across the 5 of their papers we have counts for
5 papers
AIC-VDS: Attention-Based In-Context Learning for Joint Velocity Control and Data Collection Scheduling in Multi-UAV-Assisted Pipeline Monitoring
Yousef Emami, Miguel Gutierrez Gaitan, Atefeh Hajijamali Arani +2
Uncrewed aerial vehicles (UAVs) are increasingly deployed for autonomous inspection and sensor data collection in large-scale infrastructure monitoring applications, such as pipeli…
A Novel Defense Against Poisoning Attacks on Federated Learning: LayerCAM Augmented with Autoencoder
Jingjing Zheng, Xin Yuan, Kai Li +3
Recent attacks on federated learning (FL) can introduce malicious model updates that circumvent widely adopted Euclidean distance-based detection methods. This paper proposes a nov…
Leverage Variational Graph Representation For Model Poisoning on Federated Learning
Kai Li, Xin Yuan, Jingjing Zheng +3
This paper puts forth a new training data-untethered model poisoning (MP) attack on federated learning (FL). The new MP attack extends an adversarial variational graph autoencoder…
Data-Agnostic Model Poisoning against Federated Learning: A Graph Autoencoder Approach
Kai Li, Jingjing Zheng, Xin Yuan +3
This paper proposes a novel, data-agnostic, model poisoning attack on Federated Learning (FL), by designing a new adversarial graph autoencoder (GAE)-based framework. The attack re…
Exploring Deep Reinforcement Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Persevering EdgeIoT
Jingjing Zheng, Kai Li, Naram Mhaisen +3
Federated learning (FL) has been increasingly considered to preserve data training privacy from eavesdropping attacks in mobile edge computing-based Internet of Thing (EdgeIoT). On…