Multi-Participant Multi-Class Vertical Federated Learning
This work addresses a scalability and applicability gap in VFL for multi-class problems involving multiple parties, though it appears incremental as it extends existing ideas like multi-view learning.
The paper tackles the limitation of vertical federated learning (VFL) to two participants and binary classification by proposing a multi-participant, multi-class VFL framework (MMVFL) that enables privacy-preserving label sharing. Experiments on real-world datasets show that MMVFL matches the multi-class classification performance of existing approaches.
Federated learning (FL) is a privacy-preserving paradigm for training collective machine learning models with locally stored data from multiple participants. Vertical federated learning (VFL) deals with the case where participants sharing the same sample ID space but having different feature spaces, while label information is owned by one participant. Current studies of VFL only support two participants, and mostly focus on binaryclass logistic regression problems. In this paper, we propose the Multi-participant Multi-class Vertical Federated Learning (MMVFL) framework for multi-class VFL problems involving multiple parties. Extending the idea of multi-view learning (MVL), MMVFL enables label sharing from its owner to other VFL participants in a privacypreserving manner. To demonstrate the effectiveness of MMVFL, a feature selection scheme is incorporated into MMVFL to compare its performance against supervised feature selection and MVL-based approaches. Experiment results on real-world datasets show that MMVFL can effectively share label information among multiple VFL participants and match multi-class classification performance of existing approaches.