FedBeam: Reliable Incentive Mechanisms for Federated Learning in UAV-Enabled Internet of Vehicles

Unmanned aerial vehicles (UAVs) can be utilized as airborne base stations to deliver wireless communication and federated learning (FL) training services for ground vehicles. However, most existing studies assume that vehicles (clients) and UAVs (model owners) offer services voluntarily. In reality,...

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Bibliographic Details
Published in:Drones (Basel) 2024-10, Vol.8 (10), p.567
Main Authors: Hu, Gangqiang, Zhu, Donglin, Shen, Jiaying, Hu, Jialing, Han, Jianmin, Li, Taiyong
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial intelligence
Clients
Collaboration
Communication
Cooperation
Data collection
Design
Drones
Energy consumption
Energy efficiency
Federated learning
Game theory
General Data Protection Regulation
Heterogeneity
incentive mechanism
Incentives
Information sharing
Internet
Internet of Vehicles
Machine learning
Pareto optimum
Privacy
reputation management
Reputations
Unmanned aerial vehicles
Vehicles
Wireless communications
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Summary:Unmanned aerial vehicles (UAVs) can be utilized as airborne base stations to deliver wireless communication and federated learning (FL) training services for ground vehicles. However, most existing studies assume that vehicles (clients) and UAVs (model owners) offer services voluntarily. In reality, participants (FL clients and model owners) are selfish and will not engage in training without compensation. Meanwhile, due to the heterogeneity of participants and the presence of free-riders and Byzantine behaviors, the quality of vehicles’ model updates can vary significantly. To incentivize participants to engage in model training and ensure reliable outcomes, this paper designs a reliable incentive mechanism (FedBeam) based on game theory. Specifically, we model the cooperation problem between model owners and clients as a two-layer Stackelberg game and prove the existence and uniqueness of the Stackelberg equilibrium (SE). For the cooperation among model owners, we formulate the problem as a coalition game and based on this, analyze and design a coalition formation algorithm to derive the Pareto optimal social utility. Additionally, to achieve reliable FL model updates, we design a weighted-beta (Wbeta) reputation update mechanism to incentivize FL clients to provide high-quality model updates. The experimental results show that compared to the baselines, the proposed incentive mechanism improves social welfare by 17.6% and test accuracy by 5.5% on simulated and real datasets, respectively.
ISSN:2504-446X
2504-446X
DOI:10.3390/drones8100567