Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approach (2004.03877v1)

Abstract: Coupled with the rise of Deep Learning, the wealth of data and enhanced computation capabilities of Internet of Vehicles (IoV) components enable effective 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.

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

The paper "Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approach" focuses on integrating Unmanned Aerial Vehicles (UAVs) with Federated Learning (FL) in the context of an Internet of Vehicles (IoV) framework. The research addresses the challenge of data privacy in collaborative machine learning when UAVs are utilized as service providers in IoV applications.

Key Contributions

1. UAV-Integrated IoV with Federated Learning: The authors propose leveraging UAVs for data collection and model training while ensuring data privacy through FL. This method circumvents data sharing, instead employing the aggregation of model parameters, which is crucial given stringent data privacy regulations, such as GDPR.
2. Incentive Mechanism: A noteworthy aspect of the paper is the deployment of a multi-dimensional contract-theoretic approach, which ensures truthful reporting of UAV characteristics like sensing, computation, and transmission costs. The contract-based approach is designed to align incentives between independent UAV providers and the model owner, overcoming the issue of information asymmetry.
3. Gale-Shapley Matching Algorithm: The paper utilizes the Gale-Shapley matching algorithm to assign the most cost-effective UAV to specific subregions based on their operational costs. This approach optimizes the allocation of resources, ensuring that the model owner can maximize profits while still retaining incentive compatibility under conditions of asymmetric information.
4. Efficiency and Profitability: Through simulation results, the paper demonstrates that the proposed contract design and matching mechanisms are both incentive-compatible and efficient. The findings indicate that profitability can be maximized for model owners by this optimization scheme amid significant information asymmetry between the parties involved.

Implications and Future Directions

The integration of FL with UAVs in IoV applications offers a promising avenue for developing intelligent transport systems without compromising data privacy. By not requiring raw data transfer, the proposed approach not only preserves privacy but also improves communication efficiency.

Theoretical implications include advancing contract theory applications in UAV operations and enhancing matching algorithms for dynamic network conditions. Practically, this research could lead to better resource allocation and operational efficiency in urban transport management, utilizing distributed machine learning frameworks.

Looking forward, it is plausible that more sophisticated contract designs may be developed, incorporating broader and more complex network scenarios with increasingly diverse UAV capabilities. Further exploration could also include enhancing the adaptability of matching algorithms to cater to rapidly shifting network requirements and data sensitivity concerns.

Overall, the research provides a substantial contribution to the development of privacy-preserving collaborative learning frameworks, demonstrating a viable pathway for integrating advanced AI models in real-world IoT environments.