The invention discloses a multi-period traffic prediction method based on federated learning, and the method employs a federated learning technology to allow clients in different regions to share an intermediate result of model training while maintaining data localization, thereby achieving the sharing and optimization of knowledge. Each client divides a day into a plurality of time periods and independently processes and analyzes the data of each time period, so that the model can capture unique traffic characteristics of each time period, the adaptability of the model to the traffic characteristics of different areas and time periods is improved, and the generalization ability of the model is enhanced. According to the method, privacy protection, multi-period adaptability and collaborative optimization of global and local models can be realized, and meanwhile, the prediction precision is higher.
本发明公开一种基于联邦学习的多时段交通预测方法,该方法利用联邦学习技术,允许不同区域的客户端在保持数据本地化的同时,共享模型训练的中间结果,实现知识的共享和优化。每个客户端通过将一天划分为多个时间段,并对每个时间段的数据进行单独处理和分析,使得模型能够捕捉每个时段的独特交通特征,从而提高模型对不同区域和时间段交通特征的适应性,增强模型的泛化能力。本发明的方法能够实现隐私保护、多时段适应性以及全局与局部模型的协同优化的同时,预测的精度更高。
Multi-period traffic prediction method based on federated learning
一种基于联邦学习的多时段交通预测方法
2025-01-14
Patent
Electronic Resource
Chinese
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