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

Abstract

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.

arXiv comment14 pages

References in corpus (5)

• Communication-Efficient Learning of Deep Networks from Decentralized Data
• Dynamic Computation Offloading for Mobile-Edge Computing with Energy Harvesting Devices
• Federated Optimization: Distributed Machine Learning for On-Device Intelligence
• Federated Learning in Mobile Edge Networks: A Comprehensive Survey
• Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarms

Cited by in corpus (6)

• FedML: A Research Library and Benchmark for Federated Machine Learning
• A Comprehensive Survey of Incentive Mechanism for Federated Learning
• A Comprehensive Survey of 6G Wireless Communications
• A Survey of Coded Distributed Computing
• Fully Decentralized Federated Learning Based Beamforming Design for UAV Communications
• UAV-assisted Online Machine Learning over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approach