A Survey on Contribution Evaluation in Vertical Federated Learning

arXiv:2405.02364v1 Announce Type: new
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 …

arxiv cs.dc cs.lg evaluation federated learning survey type