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- Deep Graph-based Reinforcement Learning for Joint Cruise Control and Task Offloading for Aerial Edge Internet-of-Things (EdgeIoT)Publication . Li, Kai; Ni, Wei; Yuan, Xin; Noor, Alam; Jamalipour, AbbasThis paper puts forth an aerial edge Internet-of-Things (EdgeIoT) system, where an unmanned aerial vehicle (UAV) is employed as a mobile edge server to process mission-critical computation tasks of ground Internet-of-Things (IoT) devices. When the UAV schedules an IoT device to offload its computation task, the tasks buffered at the other unselected devices could be outdated and have to be cancelled. We investigate a new joint optimization of UAV cruise control and task offloading allocation, which maximizes tasks offloaded to the UAV, subject to the IoT device’s computation capacity and battery budget, and the UAV’s speed limit. Since the optimization contains a large solution space while the instantaneous network states are unknown to the UAV, we propose a new deep graph-based reinforcement learning framework. An advantage actor-critic (A2C) structure is developed to train the real-time continuous actions of the UAV in terms of the flight speed, heading, and the offloading schedule of the IoT device. By exploring hidden representations resulting from the network feature correlation, our framework takes advantage of graph neural networks (GNN) to supervise the training of UAV’s actions in A2C. The proposed GNN-A2C framework is implemented with Google Tensorflow. The performance analysis shows that GNN-A2C achieves fast convergence and reduces considerably the task missing rate in aerial EdgeIoT.
- Poisoning federated learning with graph neural networks in Internet of DronesPublication . Li, Kai; NOOR, ALAM; Ni, Wei; Tovar, Eduardo; Fu, Xiaoming; Akan, Ozgur B.Internet of Drones (IoD) is an innovative technology that integrates mobile computing capabilities with drones, enabling them to process data at or near the location where it is collected. Federated learning can significantly enhance the efficiency and effectiveness of data processing and decision-making in IoD. Since federated learning relies on aggregating updates from multiple drones, a malicious drone can generate poisoning local model updates that involves erroneous information, leading to incorrect decisions or even dangerous situations. In this paper, a new data-independent model poisoning attack is developed to manipulate federated learning accuracy, which does not rely on training data at drones. The proposed attack leverages an adversarial graph neural network (A-GNN) to generate poisoning local model updates based on the benign local models overheard. Particularly, the A-GNN discerns the graph structural correlations between the benign local models and the features of the training data that underpin these models. The graph structural correlations are reconstructively manipulated at the malicious drone to crafts poisoning local model updates, where the training loss of the federated learning is maximized.