Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approach

@article{Lim2021TowardsFL,
  title={Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approach},
  author={Wei Yang Bryan Lim and Jianqiang Huang and Zehui Xiong and Jiawen Kang and Dusit Tao Niyato and Xiansheng Hua and Cyril Leung and Chunyan Miao},
  journal={IEEE Transactions on Intelligent Transportation Systems},
  year={2021},
  volume={22},
  pages={5140-5154}
}
Coupled with the rise of Deep Learning, the wealth of data and enhanced computation capabilities of Internet of Vehicles (IoV) components enable effective Artificial Intelligence (AI) based models to be built. Beyond ground data sources, Unmanned Aerial Vehicles (UAVs) based service providers for data collection and AI model training, i.e., Drones-as-a-Service (DaaS), is becoming increasingly popular in recent years. However, the stringent regulations governing data privacy potentially impedes… 

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