A corporate credit evaluation method considering strong feature privacy with non-private label: A vertical heterogeneous feature fusion approach

In modern monitoring and operational management, whether in industrial systems, financial risk control, or infrastructure maintenance, decision-making increasingly relies on integrating heterogeneous data from multiple sources. However, due to data privacy regulations, distributed storage, communication constraints, and sensor failures, it is often difficult to centralize modeling when dealing with high-dimensional, incomplete datasets held by different institutions. Federated learning offers a privacy-preserving joint modeling solution, yet still faces challenges such as high communication overhead, low robustness to participant dropout, and risks of gradient leakage. In certain incomplete-data scenarios, not all data is private—labels such as equipment inspection results, fault reports, or corporate blacklists and whitelists published by authoritative bodies may be public—while feature data remains private and partially missing. To address this, we propose an innovative collaborative modeling framework tailored for incomplete-data monitoring and operations, in which each participant independently trains a model on its private features and exchanges only prediction results rather than gradients. Inspired by collective expert scoring, each “expert” evaluates based on its own data, then shares scores that are integrated into a comprehensive assessment. This approach offers multiple advantages: independent model training for each party, improved efficiency by migrating only prediction results, enhanced security by avoiding gradient transmission, and higher robustness since the failure of one participant does not halt others’ training. We present three variants of this prediction-result fusion method and evaluate them on representative datasets, including enterprise credit risk assessment as a case study, comparing against vertical federated logistic regression. Experimental results validate the effectiveness of the proposed approach, which can be widely applied to diverse monitoring and operational scenarios under incomplete data conditions. • The VHFFLR framework enables collaborative modeling under incomplete-data conditions with public labels and private features. • We avoid gradient transmission, reducing training time from hours to seconds, while maintaining the accuracy over encrypted VFL. • We ensure privacy preservation and scalable applicability across domains such as credit risk assessment, anomaly detection, and predictive maintenance.