A Practical Federated Learning Framework With Truthful Incentive in UAV-Assisted Crowdsensing

Abstract

The integration of unmanned aerial vehicles (UAVs) and artificial intelligence (AI) has garnered significant interest as a promising paradigm for facilitating intelligent and pervasive mobile crowdsensing (MCS) services. In traditional AI methodologies, the centralization of large volumes of privacy-sensitive sensory data shared by UAVs for model training entails substantial privacy risks. Federated learning (FL) emerges as an appealing privacy-preserving paradigm that enables participating UAVs to collaboratively train shared models while safeguarding the privacy of their data. However, given that the execution of FL tasks inherently requires the consumption of resources such as power and bandwidth, rational and self-interested UAVs may not actively engage in FL or launch free-riding attacks (i.e., sharing fake local models) to mitigate costs. To address the above challenges, we propose a truthful incentive scheme in FL-based UAV-assisted MCS. Specifically, we first present a learning framework tailored for realistic scenarios in UAV-assisted MCS that enhances privacy preservation and optimizes communication efficiency during AI model training for collaborative UAVs, where the sensing platform (i.e., the aggregation server) is the finite-rational decision maker. Then, based on prospect theory (PT), we design an incentive mechanism to motivate UAVs to participate in FL. In this mechanism, a PT-based game is exploited to model the interactions between the sensing platform and UAVs, where the equilibrium is derived. Moreover, we employ a zero-payment mechanism to curb the self-interested behavior of UAVs. Finally, simulation results show that the proposed scheme can facilitate high-quality model sharing while suppressing free-riding attacks.