An Incentive Mechanism for Long-Term Federated Learning in Autonomous Driving
Yuchuan Fu, Zhenyu Li, Sha Liu, Changle Li, F. Richard Yu, Nan Cheng
Abstract
FL enables collaborative training of autonomous driving models without sharing the original data. It enhances the model’s environmental adaptability and establishes an effective distributed paradigm for connected and autonomous vehicles (CAVs) to share driving experiences as well as make collaborative decisions. However, participants’ negative behavior, such as free riding due to selfishness, can significantly reduce federated learning (FL) training efficiency and model accuracy. Unlike previous studies that focused solely on a single FL task, this article proposes an incentive mechanism for long-term driving model training, which models the interactions between participants and the server during the long-term FL process as an infinitely repeated game. The incentive mechanism considers the relationship between participants’ historical behaviors and their future incomes, motivating participants to maintain positive behaviors throughout the long-term FL process and ensuring the efficient operation of the training process. Furthermore, in order to increase CAVs’ enthusiasm, we design reward rules that attract new participants and encourage sustained engagement. The simulation results demonstrate that the proposed incentive mechanism maximizes the profits of both CAVs and the server in long-term FL, which effectively reduces negative CAVs’ behaviors and improves the efficiency of FL training.