车网互动(vehicle to grid，V2G)技术利用调度模型生成的决策调度电动汽车(electric vehicle，EV)有序参与电网管理，可实现高效削峰填谷，采用联邦学习方式可以在充电站不愿共享原始数据的条件下完成调度模型训练，因此选定符合多方利益的训练标签和保证模型参数聚合结果的正确性对于V2G调度决策至关重要。为此，提出一种面向V2G调度的可信联邦学习方法。首先，构建V2G实时调度模型可信联邦学习架构，其包括标签生成模块、可验证联邦学习模块和实时调度模块3个部分；然后，综合考虑EV用户、运营商及电网侧负荷波动，提出一个计及电网多方主体利益的实时调度标签数据生成模型，并设计调度模型标签的动态更新方法；其次，提出模型参数聚合的安全存证与验证方法，确保联邦学习模型参数聚合的正确性；最后，对3种充电时段类型EV占主导生成的标签数据和所提出验证方法的时间开销、存储开销和Gas开销进行分析。算例结果表明，所提出的标签模型展示了EV用户、运营商以及电网侧负荷波动的最优值特征，构建聚合树的时间开销达到毫秒级，相比于传统验证方式，聚合验证智能合约的Gas开销显著降低。因此，所提出的可信联邦学习方法与电网中多方主体利益一致，并具有较好的性能。

Vehicle-to-grid (V2G) technology utilizes scheduling models to generate decision schedules that enable electric vehicles (EVs) to participate in grid management in an orderly manner, which can achieve efficient peak shaving and valley filling. The federated learning approach can be used to train the scheduling model under the condition that the charging stations are unwilling to share the raw data. Therefore, selecting training labels that align with the interests of multiple parties and ensuring the correctness of model parameter aggregation are crucial for V2G scheduling decisions. In this paper, a trusted federated learning method for V2G scheduling is proposed. First, a trusted federated learning architecture for real-time V2G scheduling is constructed, which includes three components: a label generation module, a verifiable federated learning module, and a real-time scheduling module. Next, a real-time scheduling label data generation model is proposed, considering the interests of EV users, operators, and grid-side load fluctuations, and a dynamic updating method for scheduling model labels is designed. Furthermore, a secure record and verification method for model parameter aggregation is proposed to ensure the correctness of federated learning model parameter aggregation. Finally, the time overhead, storage overhead, and Gas cost of the label data generated by EVs in three types of charging time periods and the proposed verification method are analyzed. Numerical results indicate that the proposed label model exhibits optimal values for EV users, operators, and grid-side load fluctuations, with the time overhead for constructing the aggregation tree reaching the millisecond level. Compared to traditional verification methods, the Gas cost of aggregating and verifying using the smart contract is significantly reduced. Thus, the proposed trusted federated learning method aligns with the interests of multiple stakeholders in the power grid and demonstrates superior performance.