Vertical Federated Learning (VFL) has emerged as a critical approach in machine learning to address privacy concerns associated with centralized data storage and processing. VFL facilitates collaboration among multiple entities with distinct feature sets on the same user population, enabling the joint training of predictive models without direct data sharing. A key aspect of VFL is the fair and accurate evaluation of each entity's contribution to the learning process. This is crucial for maintaining trust among participating entities, ensuring equitable resource sharing, and fostering a sustainable collaboration framework. This paper provides a thorough review of contribution evaluation in VFL. We categorize the vast array of contribution evaluation techniques along the VFL lifecycle, granularity of evaluation, privacy considerations, and core computational methods. We also explore various tasks in VFL that involving contribution evaluation and analyze their required evaluation properties and relation to the VFL lifecycle phases. Finally, we present a vision for the future challenges of contribution evaluation in VFL. By providing a structured analysis of the current landscape and potential advancements, this paper aims to guide researchers and practitioners in the design and implementation of more effective, efficient, and privacy-centric VFL solutions. Relevant literature and open-source resources have been compiled and are being continuously updated at the GitHub repository: \url{https://github.com/cuiyuebing/VFL_CE}.

翻译：纵向联邦学习（VFL）已成为机器学习中应对集中式数据存储与处理隐私问题的关键方法。VFL支持具有不同特征集的多个实体在同一用户群体上协作，从而在不直接共享数据的情况下联合训练预测模型。VFL的核心在于公平且准确地评估每个实体在学习过程中的贡献。这对于维护参与实体间的信任、确保资源公平共享以及构建可持续协作框架至关重要。本文对VFL中的贡献评估进行了全面综述。我们根据VFL生命周期、评估粒度、隐私考量及核心计算方法对现有贡献评估技术进行分类。同时，探讨了VFL中涉及贡献评估的各类任务，分析了其所需的评估属性以及与VFL生命周期阶段的关联。最后，展望了VFL中贡献评估的未来挑战。通过系统分析当前研究现状与潜在进展，本文旨在为研究人员和实践者设计更高效、更准确且更注重隐私的VFL解决方案提供指导。相关文献与开源资源已整理并持续更新于GitHub仓库：\url{https://github.com/cuiyuebing/VFL_CE}。