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

arXiv: https://arxiv.org/abs/2004.03877 · HTML: https://mlsanity.com/paper/2004.03877/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2020.

## Built on by (most cited first, 29 of 29)

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- [UAV-assisted Online Machine Learning over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approach](https://mlsanity.com/paper/2106.15734/) (2021)
- [FedICT: Federated Multi-task Distillation for Multi-access Edge Computing](https://mlsanity.com/paper/2301.00389/) (2023)
- [Exploring Deep Reinforcement Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Persevering EdgeIoT](https://mlsanity.com/paper/2202.07391/) (2022)
- [Robust Semi-supervised Federated Learning for Images Automatic Recognition in Internet of Drones](https://mlsanity.com/paper/2201.01230/) (2022)
- [A Systematic Literature Review on Blockchain Enabled Federated Learning Framework for Internet of Vehicles](https://mlsanity.com/paper/2203.05192/) (2022)
- [Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directions](https://mlsanity.com/paper/2110.04160/) (2021)
- [Online Auction-Based Incentive Mechanism Design for Horizontal Federated Learning with Budget Constraint](https://mlsanity.com/paper/2201.09047/) (2022)
- [Stochastic Coded Federated Learning: Theoretical Analysis and Incentive Mechanism Design](https://mlsanity.com/paper/2211.04132/) (2022)
- [Federated Learning for Cyber Physical Systems: A Comprehensive Survey](https://mlsanity.com/paper/2505.04873/) (2025)
- [Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights](https://mlsanity.com/paper/2312.04688/) (2023)
- [Characterization of the Global Bias Problem in Aerial Federated Learning](https://mlsanity.com/paper/2212.14360/) (2022)
- [Collaborative Learning with a Drone Orchestrator](https://mlsanity.com/paper/2303.02266/) (2023)
- [Integrated Sensing, Computation, and Communication for UAV-assisted Federated Edge Learning](https://mlsanity.com/paper/2306.02990/) (2023)
- [Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applications](https://mlsanity.com/paper/2310.05269/) (2023)
- [An Efficient and Reliable Asynchronous Federated Learning Scheme for Smart Public Transportation](https://mlsanity.com/paper/2208.07194/) (2022)
- [FedCL: Federated Multi-Phase Curriculum Learning to Synchronously Correlate User Heterogeneity](https://mlsanity.com/paper/2211.07248/) (2022)
- [An Auction-based Marketplace for Model Trading in Federated Learning](https://mlsanity.com/paper/2402.01802/) (2024)
- [Nothing Wasted: Full Contribution Enforcement in Federated Edge Learning](https://mlsanity.com/paper/2110.08330/) (2021)
- [Efficient Core-selecting Incentive Mechanism for Data Sharing in Federated Learning](https://mlsanity.com/paper/2309.11722/) (2023)
- [BPFISH: Blockchain and Privacy-preserving FL Inspired Smart Healthcare](https://mlsanity.com/paper/2207.11654/) (2022)
- [Towards Realistic Mechanisms That Incentivize Federated Participation and Contribution](https://mlsanity.com/paper/2310.13681/) (2023)
- [FedCross: Intertemporal Federated Learning Under Evolutionary Games](https://mlsanity.com/paper/2412.16968/) (2024)
- [Efficient UAV Swarm-Based Multi-Task Federated Learning with Dynamic Task Knowledge Sharing](https://mlsanity.com/paper/2503.09144/) (2025)
- [Latency-aware Multimodal Federated Learning over UAV Networks](https://mlsanity.com/paper/2510.01717/) (2025)

## 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.