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

被引:200
作者
Lim, Wei Yang Bryan [1 ,2 ]
Huang, Jianqiang [1 ]
Xiong, Zehui [3 ]
Kang, Jiawen [4 ]
Niyato, Dusit [4 ]
Hua, Xian-Sheng [1 ]
Leung, Cyril [5 ,6 ]
Miao, Chunyan [4 ,6 ]
机构
[1] Alibaba Grp, Singapore 068811, Singapore
[2] Nanyang Technol Univ NTU, Alibaba NTU Joint Res Inst, Singapore 639798, Singapore
[3] Singapore Univ Technol & Design, Pillar Informat Syst Technol & Design, Singapore 487372, Singapore
[4] Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore 639798, Singapore
[5] Univ British Columbia, Dept Elect & Comp Engn, Vancouver, BC V6T 1Z4, Canada
[6] Joint NTU UBC Res Ctr Excellence Act Living Elder, Singapore 639798, Singapore
基金
新加坡国家研究基金会;
关键词
Sensors; Computational modeling; Data models; Unmanned aerial vehicles; Contracts; Collaborative work; Training; Federated learning; incentive mechanism; unmanned aerial vehicles; contract theory; matching; COLLECTION; NETWORKS; TRACKING; ROAD;
D O I
10.1109/TITS.2021.3056341
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
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.
引用
收藏
页码:5140 / 5154
页数:15
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