发表机构
Bangladesh University of Engineering and Technology; University of New England; Ulster University（孟加拉国工程技术大学; 新英格兰大学; 阿尔斯特大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对电信异构数据下客户流失预测的隐私问题，提出DP-FedProx差分隐私联邦近端优化框架，在公开数据集上以略降精度实现与集中式相当的预测性能，并兼顾隐私保护。
AI 中文摘要
客户流失是电信行业面临的主要问题之一。为预测客户流失，传统的集中式机器学习方法已被广泛使用。这种集中式方法要求将客户数据存储在中央存储库中，这引发了隐私担忧，并可能违反数据保护法规。联邦学习通过允许多个电信运营商在不传输其原始客户数据的情况下协作训练全局模型来解决此问题。然而，现实世界中的客户数据通常是异构的（非独立同分布），这可能对标准联邦学习的性能产生负面影响。训练后的模型也可能遭受隐私攻击。为解决这些问题，我们提出了一种基于差分隐私（DP）的联邦近端优化（FedProx）框架。所有实验均在两个公开可用的电信流失数据集上进行。我们训练了联邦平均（FedAvg）、DP-FedAvg、FedProx以及所提出的DP-FedProx框架。为进行基线比较，我们还使用了若干集中式和本地模型。为评估模型，我们采用了七种广泛使用的评估指标。实验结果表明，基于FedProx的模型始终优于基于FedAvg的模型。与最佳集中式模型相比，所提出的DP-FedProx框架在提供隐私保证的同时，仅以较小的准确率下降实现了具有竞争力的预测性能。为解释我们的模型，我们进行了SHAP分析，结果表明DP-FedProx方法优先考虑收入组特征。这些结果表明，所提出的DP-FedProx框架在预测性能和数据隐私保护之间提供了实用的平衡。
英文摘要
Customer churn is one of the major issues in the telecommunication industry. To predict customer churn, conventional centralized machine learning approaches have been widely used. This centralized approach requires customer data to be stored in a central repository, which raises privacy concerns and may violate data protection regulations. Federated learning addresses this problem by allowing multiple telecom operators to collaboratively train a global model without transferring their raw customer data. However, real-world customer data are often heterogeneous (non-IID), which may negatively affect the performance of standard federated learning. Trained models can also suffer from privacy attacks. To address those issues, we propose a Differentially Private (DP) based Federated Proximal optimization (FedProx) framework. All experiments were performed on two publicly available telecom churn datasets. We trained Federated Averaging (FedAvg), DP-FedAvg, FedProx, and the proposed DP-FedProx framework. For baseline comparison, we also used several centralized and local models. To evaluate the models, we employed seven widely used evaluation metrics. The experimental results show that the FedProx based models consistently outperform the FedAvg based models. Compared with the best centralized model, the proposed DP-FedProx framework achieves competitive prediction performance with only a small reduction in accuracy while providing privacy guarantees. To explain our model, we conducted SHAP analysis which shows that DP-FedProx method priorities revenue group features. These results indicate that the proposed DP-FedProx framework provides a practical balance between prediction performance and data privacy protection.
