A Survey on Contribution Evaluation in Vertical Federated Learning

TL;DR

This survey reviews current methods for evaluating contributions in vertical federated learning, highlighting challenges, categorizing techniques, and proposing future research directions to improve fairness, privacy, and effectiveness.

Contribution

It provides a comprehensive categorization and analysis of contribution evaluation techniques in VFL, along with a future outlook and resource compilation.

Findings

Categorized contribution evaluation methods along VFL lifecycle

Identified privacy considerations in evaluation techniques

Outlined future challenges and research directions

Abstract

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}.