Deep Latent Variable Model based Vertical Federated Learning with Flexible Alignment and Labeling Scenarios

ArXi:2505.11035v2 Announce Type: replace Federated learning (FL) has attracted significant attention for enabling collaborative learning without exposing private data. Among the primary variants of FL, vertical federated learning (VFL) addresses feature-partitioned data held by multiple institutions, each holding complementary information for the same set of users. However, existing VFL methods often impose restrictive assumptions such as a small number of participating parties, fully aligned data, or only using labeled data.