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Title: Towards federated learning in UAV-enabled internet of vehicles : a multi-dimensional contract-matching approach
Authors: Lim, Bryan Wei Yang
Huang, Jianqiang
Xiong, Zehui
Kang, Jiawen
Niyato, Dusit
Hua, Xian-Sheng
Leung, Cyril
Miao, Chunyan
Keywords: Engineering::Computer science and engineering
Issue Date: 2021
Source: Lim, B. W. Y., Huang, J., Xiong, Z., Kang, J., Niyato, D., Hua, X., Leung, C. & Miao, C. (2021). Towards federated learning in UAV-enabled internet of vehicles : a multi-dimensional contract-matching approach. IEEE Transactions On Intelligent Transportation Systems, 22(8), 5140-5154.
Project: AISG-GC-2019-003
M4082187 (4080)
Journal: IEEE Transactions on Intelligent Transportation Systems
Abstract: 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.
ISSN: 1524-9050
DOI: 10.1109/TITS.2021.3056341
Rights: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at:
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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