OPUS-VFL: Incentivizing Optimal Privacy-Utility Tradeoffs in Vertical Federated Learning

ArXi:2504.15995v2 Announce Type: replace-cross Vertical Federated Learning (VFL) enables organizations with disjoint feature spaces but shared user bases to collaboratively train models without sharing raw data. However, existing VFL systems face critical limitations: they often lack effective incentive mechanisms, struggle to balance privacy-utility tradeoffs, and fail to accommodate clients with heterogeneous resource capabilities. These challenges hinder meaningful participation, degrade model performance, and limit practical deployment.