Research Area: Vehicular Ad Hoc Networks
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 data sharing across independently owned UAVs. To this end, we propose the adoption of a Federated Learning (FL) based approach to enable privacy-preserving collaborative Machine Learning across a federation of independent DaaS providers for the development of IoV applications, e.g., for traffic prediction and car park occupancy management. Given the information asymmetry and incentive mismatches between the UAVs and model owners, we leverage on the self-revealing properties of a multi-dimensional contract to ensure truthful reporting of the UAV types, while accounting for the multiple sources of heterogeneity, e.g., in sensing, computation, and transmission costs. Then, we adopt the Gale-Shapley algorithm to match the lowest cost UAV to each subregion. The simulation results validate the incentive compatibility of our contract design, and shows the efficiency of our matching, thus guaranteeing profit maximization for the model owner amid information asymmetry.
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Author(s) Name: Wei Yang Bryan Lim; Jianqiang Huang; Zehui Xiong; Jiawen Kang; Dusit Niyato; Xian-Sheng Hua; Cyril Leung; Chunyan Miao
Journal name: IEEE Transactions on Intelligent Transportation Systems
Conferrence name:
Publisher name: IEEE
DOI: 10.1109/TITS.2021.3056341
Volume Information: ( Volume: 22, Issue: 8, Aug. 2021) Page(s): 5140 - 5154
