Multi-dimensional Contract Matching Design for Federated Learning in UAV Networks

Abstract

AbstractThe 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 (DaaS), are becoming increasingly popular in recent years. However, the stringent regulations governing data privacy potentially impede data sharing across independently owned UAVs. In this chapter, 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. 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 show the efficiency of our matching, thus guaranteeing profit maximization for the model owner amid information asymmetry.KeywordsFederated learningEdge intelligenceUnmanned aerial vehicleIncentive mechanismContract theoryGame theory