VFDV-IM: An Efficient and Securely Vertical Federated Data Valuation

Xiaokai Zhou, Xiao Yan, Xinyan Li, Hao Huang, Quanqing Xu, Qinbo Zhang, Yen Jerome, Zhaohui Cai, Jiawei Jiang

Abstract: Vertical federated learning enables multiple participants to build a joint machine learning model upon distributed features of overlapping samples. The performance of VFL models heavily depends on the quality of participants’ local data. It’s essential to measure the contributions of the participants for various purposes, e.g., participant selection and reward allocation. The Shapley value is widely adopted by previous works for contribution assessment. However, computing the Shapley value in VFL requires repetitive model training from scratch, incurring expensive computation and communication overheads. Inspired by this challenge, in this paper, we ask: can we efficiently and securely perform data valuation for participants via the Shapley value in VFL?