| 基于联邦学习的自助取货机远程下单数据共享方法 |
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| 引用本文：赵峻岭，梁峰，陈琳.基于联邦学习的自助取货机远程下单数据共享方法[J].计算技术与自动化,2024,(3):141-147 |
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| 中文摘要:研究了基于联邦学习的自助取货机远程下单数据共享方法。精准有效共享自助取货机各端口运营中的下单数据，为有效分析各端口营销差异、保障其合理运营提供依据。运用自编码神经网络改进基础联邦学习模型，获得半监督联邦学习模型，结合增量加权训练该模型后，运用训练后的半监督联邦学习模型共享各自助取货机端口的远程下单数据。结果显示，该方法可有效共享各远程自助取货机端口的下单数据，依据共享数据可有效分析出各端口不同时段的畅销品类；当共享中存在无标记数据端口，且通信轮数较低时，该方法的共享精度略受影响，而通信轮数到达一定数量后，该方法的共享精度稳定不受此因素干扰；当共享中存在端口新增下单数据时，新增的下单数据量对该方法的共享精度几乎无影响。 |
| 中文关键词:联邦学习 自助取货机 远程下单数据 自编码 半监督 增量加权 |
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| Remote Order Data Sharing Method for Self-Service Cargo Machine Based on Federated Learning |
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| Abstract:This paper studies the remote order data sharing method of self-pick-up machine based on federal learning, accurately and effectively shares the order data of each port operation of self-pick-up machine, and provides a basis for effectively analyzing the marketing differences of each port and ensuring its reasonable operation. The self-coding neural network was used to improve the basic federated learning model, and the semi-supervised federated learning model was obtained. After the model was trained with incremental weighting, the semi-supervised federated learning model was used to share the remote order data of each assisted cargo plane port. The results show that the method can effectively share the order data of each remote self-pick-up machine port, and can effectively analyze the best-selling categories of each port in different periods according to the shared data. When there are unmarked data ports and the number of communication wheels is low, the sharing accuracy of the method is slightly affected. When the number of communication wheels reaches a certain number, the sharing accuracy of the method is stable without interference from this factor. When the newly added order data exists in the sharing, the newly added order data has almost no effect on the sharing accuracy of the method. |
| keywords:federated learning self-service pickup machine remote order data self-coding semi-supervision incremental weighting |
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