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

TL;DR

This paper proposes a federated learning framework for UAV-based IoV data collection, using multi-dimensional contracts and matching algorithms to address privacy, heterogeneity, and incentive issues, enhancing collaborative AI development.

Contribution

It introduces a novel multi-dimensional contract-matching approach for incentivizing UAVs in federated learning within IoV, addressing heterogeneity and information asymmetry.

Findings

Simulation confirms incentive compatibility of the contract design.

Matching algorithm efficiently pairs UAVs to regions, maximizing profit.

Proposed method ensures privacy-preserving collaborative learning.

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.