A Distributed Incentive Mechanism to Balance Demand and Communication Overhead for Multiple Federated Learning Tasks in IoV

Federated learning (FL), as a typical distributed machine learning framework, has been effectively applied to traffic flow optimization, driving behavior analysis, and other areas. However, the stability and efficiency of FL systems heavily rely on the quality and cooperation of participants. If participants find no profit in the FL process, they may reduce their willingness to participate due to energy consumption and limited resources. To bridge these gaps, this article proposes a demand-balanced incentive mechanism for multiple FL tasks. First, considering the time-varying channel characteristics in the Internet of Vehicles (IoV) scenario, two optimization problems are constructed: 1) maximizing task-matching satisfaction and 2) minimizing communication energy consumption. Second, these problems are transformed into a distributed incentive mechanism based on a multileader-multifollower (MLMF) Stackelberg game, and a bi-level alternating direction method of multipliers (ADMM) algorithm is proposed to solve for the optimal resource allocation and reward schemes that balance the demands of all parties. Furthermore, this article designs a multiagent deep reinforcement learning-based method to solve the incentive problem, thereby avoiding the impact of information asymmetry. Simulation results verify that the proposed scheme not only balances the demands of all parties but also enhances user participation without being affected by the number of participants, making it suitable for IoV environments.