Visible to the public Incentive Mechanism Design for Federated Learning in the Internet of Vehicles

TitleIncentive Mechanism Design for Federated Learning in the Internet of Vehicles
Publication TypeConference Paper
Year of Publication2020
AuthorsLim, Wei Yang Bryan, Xiong, Zehui, Niyato, Dusit, Huang, Jianqiang, Hua, Xian-Sheng, Miao, Chunyan
Conference Name2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall)
Date Published Dec. 2020
ISBN Number978-1-7281-9484-4
Keywordsartificial intelligence, Collaboration, contracts, cyber physical systems, Data models, federated learning, Human Behavior, human factors, incentive mechanism, Intelligent vehicles, Internet, Internet of Vehicles, Metrics, Numerical models, pubcrawl, resilience, Resiliency, Vehicular Networks
AbstractIn the Internet of Vehicles (IoV) paradigm, a model owner is able to leverage on the enhanced capabilities of Intelligent Connected Vehicles (ICV) to develop promising Artificial Intelligence (AI) based applications, e.g., for traffic efficiency. However, in some cases, a model owner may have insufficient data samples to build an effective AI model. To this end, we propose a Federated Learning (FL) based privacy preserving approach to facilitate collaborative FL among multiple model owners in the IoV. Our system model enables collaborative model training without compromising data privacy given that only the model parameters instead of the raw data are exchanged within the federation. However, there are two main challenges of incentive mismatches between workers and model owners, as well as among model owners. For the former, we leverage on the self-revealing mechanism in contract theory under information asymmetry. For the latter, we use the coalitional game theory approach that rewards model owners based on their marginal contributions. The numerical results validate the performance efficiency of our proposed hierarchical incentive mechanism design.
Citation Keylim_incentive_2020