Exploring Deep-Reinforcement-Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Preserving EdgeIoT

@article{Zheng2022ExploringDF,
  title={Exploring Deep-Reinforcement-Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Preserving EdgeIoT},
  author={Jingjing Zheng and Kai Li and Naram Mhaisen and Wei Ni and Eduardo Tovar and Mohsen Guizani},
  journal={IEEE Internet of Things Journal},
  year={2022},
  volume={9},
  pages={21099-21110}
}
Federated learning (FL) has been increasingly considered to preserve data training privacy from eavesdropping attacks in mobile-edge computing-based Internet of Things (EdgeIoT). On the one hand, the learning accuracy of FL can be improved by selecting the IoT devices with large data sets for training, which gives rise to a higher energy consumption. On the other hand, the energy consumption can be reduced by selecting the IoT devices with small data sets for FL, resulting in a falling learning… 
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2 Citations

A Partial Federated Learning Model in Cognitive UAV-enabled Edge Computing Networks

A cognitive network of unmanned aerial vehicles is presented that aims to reliably offer edge computing services to Internet-of-things devices in a specific location and can outperform the FL model by 21.72%.

Compensation layer‐added federated learning receiver: Design and implementation

Simulation results are provided to demonstrate that the proposed compensation layer‐added federated receiver outperforms the conventional Federated receiver.

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