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

arXiv 2004.03877 ↗First posted 8 Apr 2020eess.SP cs.NIcited by 29

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Abstract (arXiv)

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, is 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.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Apr 2020.

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Citing papers per year: 2021: 6; 2022: 10; 2023: 7; 2024: 3; 2025: 3.

29 papers.

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  1. 2021Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspectivecited by 47
  2. 2021A Comprehensive Survey of Incentive Mechanism for Federated Learningcited by 38
  3. 2021Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AIcited by 38
  4. 2022Mechanisms that Incentivize Data Sharing in Federated Learningcited by 38
  5. 2024Advances and Open Challenges in Federated Foundation Modelscited by 33
  6. 2021UAV-assisted Online Machine Learning over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approachcited by 20
  7. 2023FedICT: Federated Multi-task Distillation for Multi-access Edge Computingcited by 17
  8. 2022Exploring Deep Reinforcement Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Persevering EdgeIoTcited by 12
  9. 2022Robust Semi-supervised Federated Learning for Images Automatic Recognition in Internet of Dronescited by 7
  10. 2022A Systematic Literature Review on Blockchain Enabled Federated Learning Framework for Internet of Vehiclescited by 7
  11. 2021Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directionscited by 6
  12. 2022Online Auction-Based Incentive Mechanism Design for Horizontal Federated Learning with Budget Constraintcited by 6
17 more
  1. 2022Stochastic Coded Federated Learning: Theoretical Analysis and Incentive Mechanism Designcited by 6
  2. 2025Federated Learning for Cyber Physical Systems: A Comprehensive Surveycited by 6
  3. 2023Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insightscited by 4
  4. 2022Characterization of the Global Bias Problem in Aerial Federated Learningcited by 3
  5. 2023Integrated Sensing, Computation, and Communication for UAV-assisted Federated Edge Learningcited by 3
  6. 2023Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applicationscited by 3
  7. 2022An Efficient and Reliable Asynchronous Federated Learning Scheme for Smart Public Transportationcited by 2
  8. 2022FedCL: Federated Multi-Phase Curriculum Learning to Synchronously Correlate User Heterogeneitycited by 2
  9. 2023Collaborative Learning with a Drone Orchestratorcited by 2
  10. 2024An Auction-based Marketplace for Model Trading in Federated Learningcited by 2
  11. 2021Nothing Wasted: Full Contribution Enforcement in Federated Edge Learningcited by 1
  12. 2023Efficient Core-selecting Incentive Mechanism for Data Sharing in Federated Learningcited by 1
  13. 2022BPFISH: Blockchain and Privacy-preserving FL Inspired Smart Healthcarecited by 0
  14. 2023Towards Realistic Mechanisms That Incentivize Federated Participation and Contributioncited by 0
  15. 2024FedCross: Intertemporal Federated Learning Under Evolutionary Gamescited by 0
  16. 2025Efficient UAV Swarm-Based Multi-Task Federated Learning with Dynamic Task Knowledge Sharingcited by 0
  17. 2025Latency-aware Multimodal Federated Learning over UAV Networkscited by 0