用可解释联邦学习提升供应链信用评估的准确与透明
Trans-XFed: An Explainable Federated Learning for Supply Chain Credit Assessment
- 结合联邦学习与可解释AI，解决数据隐私与信息孤岛问题
- 通过高F1分数客户端选择加速收敛，提升非独立同分布数据表现
- 采用梯度积分法解释决策过程，适合需要透明风控的金融场景
本文提出Trans-XFed架构，将联邦学习与可解释AI技术结合，用于供应链信用评估。针对隐私保护、信息孤岛、类别不平衡、非独立同分布（Non-IID）数据及模型可解释性等挑战，设计基于性能的客户端选择策略（PBCS），通过选取本地F1分数较高的客户端实现更快收敛。核心模型采用增强同态加密的FedProx，并引入Transformer编码器以分析特征学习过程。同时，使用集成梯度方法提供决策解释。在真实供应链数据集上的实验表明，该模型在保持隐私安全的前提下，相比多个基线方法实现了更高的信用评估准确性与决策透明性。
原文摘要 · Abstract (English)
This paper proposes a Trans-XFed architecture that combines federated learning with explainable AI techniques for supply chain credit assessment. The proposed model aims to address several key challenges, including privacy, information silos, class imbalance, non-identically and independently distributed (Non-IID) data, and model interpretability in supply chain credit assessment. We introduce a performance-based client selection strategy (PBCS) to tackle class imbalance and Non-IID problems. This strategy achieves faster convergence by selecting clients with higher local F1 scores. The FedProx architecture, enhanced with homomorphic encryption, is used as the core model, and further incorporates a transformer encoder. The transformer encoder block provides insights into the learned features. Additionally, we employ the integrated gradient explainable AI technique to offer insights into decision-making. We demonstrate the effectiveness of Trans-XFed through experimental evaluations on real-world supply chain datasets. The obtained results show its ability to deliver accurate credit assessments compared to several baselines, while maintaining transparency and privacy.
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