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摘要: 为实现兼顾偏好异质性与数据隐私化的个体出行推荐，基于模型参数化聚合与分布式训练的联邦学习计算范式，提出了一种联邦混合罗吉特(FMXL)模型，可解构标准MXL模型，以实现本地个体偏好与全局群体差异参数估算的分离；为了消除模型对原始数据的依赖，提出了标准与聚合2种联邦吉布斯抽样算法，通过本地与全局参数的交互，实现模型的层次化联合估计；为了验证所提模型与算法，基于Swiss Metro公开数据集，分别搭建了离线与在线2种出行推荐场景。分析结果表明：针对离线场景，2种联邦吉布斯抽样算法拟合的FMXL模型与标准多项式罗吉特模型相比，其对数似然值分别增大了157.8和153.2，预测率分别提升了12.3%和12.1%；与基于集中式吉布斯抽样算法拟合的MXL模型相比，其计算时间分别缩短了64.2%和76.9%，通信时间均缩短了86.2%；针对在线场景，FMXL模型的对数似然值和预测率均呈上升趋势，且整个估计过程的计算和通信时间均低于标准MXL模型。可见，以数据隐私化处理为前提，MXL模型的联邦化训练既能保证出行推荐的精准性，也能充分调动用户端闲置算力，有效提升出行推荐的时效性，体现了所提模型和算法的高适应和可拓展能力，同时基于联邦异质性模型的个体出行推荐还能有效推进交通系统的智能化进程。Abstract: To achieve personal travel recommendations by considering both the preference heterogeneity and data privacy, based on the model parametric aggregation and distributed training supported by the federated learning (FL) computing paradigm, a federated mixed Logit (FMXL) model was proposed by decoupling the standard mixed Logit model to separate the parameter estimation of local individual preferences and global population differences. In order to eliminate the dependence of the model on the original data, two federated Gibbs sampling algorithms, to be standardized or aggregated, were designed, to achieve the hierarchical estimation of the model through the interaction of local and global parameters. The proposed model and algorithm were validated for offline and online travel recommendation scenarios based on Swiss Metro data. Analysis results show that for offline scenario, the FMXL model based on two federated Gibbs sampling algorithms increase the log-likelihood value by 157.8 and 153.2, and the prediction rate by 12.3% and 12.1%, respectively, compared with the standard multinomial Logit model. In addition, the computation time reduce by 64.2% and 76.9%, respectively, and the communication times both reduce by 86.2% compared with the mixed Logit model based on the centralized Gibbs sampling algorithm. For the online scenario, both the log-likelihood value and the prediction rate of the FMXL model show an increasing trend, and the computation and communication times of the whole estimation process are lower than those of the standard mixed Logit model. Overall, with the data privacy as the precondition, the federated training of the MXL model ensures the accuracy of travel recommendations and effectively enhances the timeliness of travel recommendations by fully utilizing the idle computing power on the user side, reflecting the high adaptability and scalability of the proposed model and algorithms. In addition, the intelligent progress of the transportation system can be promoted effectively by personal travel recommendations based on the FMXL model. 3 tabs, 4 figs, 30 refs.
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图 1 个体出行隐私化推荐的联邦学习
Figure 1. Federated learning for personal travel privacy recommendation
表 1 离线场景的模型参数估计结果
Table 1. Model parameter estimation results in offline scenario
指标 标准MNL模型 FMXL模型 MNL-Gibbs FMXL-SFGibbs FMXL-AFGibbs 固定参数 μ Ω μ Ω 均值 标准差 均值 标准差 均值 标准差 均值 标准差 均值 标准差 α0 1.313 0.096 0.509 0.086 1.406 0.178 0.529 0.058 0.954 0.130 α1 1.286 0.096 0.251 0.065 0.436 0.059 0.277 0.043 0.288 0.038 βT 0.559 0.052 -0.006 0.070 1.421 0.218 -0.045 0.048 1.362 0.205 βλ 0.069 0.045 -1.920 0.197 0.915 0.139 -1.880 0.195 0.722 0.108 
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表 2 离线场景的精确性对比
Table 2. Accuracy comparison in offline scenario
指标 标准MNL模型 FMXL模型 MNL-Gibbs FMXL-SFGibbs FMXL-AFGibbs L -711.325 -553.568 -558.169 R 0.649 0.772 0.770
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表 3 离线场景的时效性对比
Table 3. Timeliness comparison in offline scenario
指标 MXL模型 FMXL模型 MXL-Gibbs FMXL-SFGibbs FMXL-AFGibbs 计算时间/s 9.305 3.331 2.151 通信时间/s 5.184 0.746 0.746 总计/s 14.489 4.077 2.897
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