A Fairness-Aware Resource Management Model With Many-Objective Optimization in Uncertain Resource-Constrained Internet of Vehicles

In federated learning (FL)-assisted Internet of Vehicles (IoV) system, joint vehicle scheduling and resource management have become an effective approach to improving FL communication efficiency. However, heterogeneity in data, varying computational capabilities, and limited resources among different vehicle devices lead to significant disparities in how frequently devices participate in FL and in their training performance. This generates unfairness during device selection, which in turn constrains overall system performance and the model’s generalization ability. To address these challenges, we propose a fairness-aware interval constrained many-objective optimization method for joint vehicle scheduling and bandwidth allocation. The proposed method considers factors, such as device accuracy, latency, server utilization, and fairness in vehicle participation while satisfying energy consumption constraints. During FL parameter aggregation, a fairness-driven aggregation strategy is designed, which adjusts aggregation weights based on a fairness indicator. Additionally, we develop an indicator-driven multilevel interval constrained many-objective evolutionary algorithm (IM-ICMaOEA) to efficiently identify the Pareto Front that satisfies the constraints. Extensive simulation results demonstrate the effectiveness of the proposed method in terms of accuracy, device delay, energy consumption, and fairness in vehicle participation, significantly enhancing training efficiency in wireless channel environments. It provides significant scientific value and application potential in the field of the IoV.