Vertical Federated Learning Across Heterogeneous Regions for Industry 4.0
Rui Ying Zhang, Hongwei Li, Luoding Tian, Meng Hao, Yuan Zhang
Abstract
This work investigates fine-grained data distribution in real-world federated learning (FL) applications, wherein training samples are distributed across multiple regions, and different clients within each region possess distinct features of local training samples. Furthermore, the datasets and models in these regions often exhibit heterogeneity, characterized by varying label distributions and model architectures, posing challenges to the model construction process. In this article, we propose a vertical federated learning (VFL) framework, named HeteroVFL, to address the data distribution complexities and overcome the hurdles posed by heterogeneous regions. Besides, we enhance the privacy of HeteroVFL by adopting differential privacy, a privacy-preserving technology by injecting measured noise into data based on a stochastic framework. We compare our HeteroVFL with existing solutions on three real-world datasets in simulations. The results demonstrate that HeteroVFL can achieve over 96% accuracy on MNIST, surpassing the accuracy of 90% in the state-of-the-art VFL benchmarks.