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:
https://github.com/cuiyuebing/VFL_CE.

通过结构化的分析，本文对垂直联邦学习 (VFL) 中的贡献评估进行了全面的回顾，包括评估技术、隐私考虑和计算方法，并探讨了在 VFL 中涉及贡献评估的各种任务及其与 VFL 生命周期阶段的关系。最后，本文提出了 VFL 贡献评估面临的未来挑战，旨在引导研究人员和实践者设计和实现更有效、高效和注重隐私的 VFL 解决方案。