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

• Published in: IEEE transactions on intelligent transportation systems Vol. 22; no. 8; pp. 5140 - 5154
• Main Authors: Lim, Wei Yang Bryan, Huang, Jianqiang, Xiong, Zehui, Kang, Jiawen, Niyato, Dusit, Hua, Xian-Sheng, Leung, Cyril, Miao, Chunyan
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accounting; Algorithms; Artificial intelligence; Asymmetry; Collaborative work; Computation; Computational modeling; contract theory; Contracts; Data collection; Data models; Data retrieval; Deep learning; Federated learning; Heterogeneity; incentive mechanism; Internet of Vehicles; Machine learning; Matching; Occupancy; Parking facilities; Privacy; Sensors; Training; Unmanned aerial vehicles
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2021.3056341

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.