Shared yet Distinct: Rethinking Label-Scarce Vertical Federated Learning with Heterogeneity-Aware Meta-Prototype

Label scarcity and aligned-sample sparsity have emerged as major barriers to the practical deployment of supervised Vertical Federated Learning (VFL). Existing self-supervised VFL frameworks designed for label-scarce settings primarily rely on instance-discriminative representation learning. However, this learning paradigm tends to enforce rigid alignment of representations in the latent space, thereby inducing severe representation collapse in VFL. Moreover, these self-supervised VFL frameworks remain heavily dependent on frequent inter-participant representation exchange and knowledge-transfer schemes, further limiting scalability in heterogeneous settings while introducing computational and security overhead. To overcome these fundamental limitations, we propose the Heterogeneity-Aware Meta-Prototype Learning (HAMPL) framework, which establishes a new paradigm for self-supervised VFL by introducing meta-prototypes as safer intermediaries. By accounting for inter-participant heterogeneity, HAMPL enables each participant model to learn shared and generalizable knowledge while preserving its distinctive participant-specific information, thereby exploiting the complementarity of heterogeneous feature subsets across participants. Furthermore, meta-prototypes act as a knowledge bridge across the two pre-training stages, naturally mitigating catastrophic forgetting that can arise from the two-stage learning paradigm. We further provide a theoretical characterization and a security analysis of HAMPL. Extensive experiments indicate that HAMPL achieves superior performance in label-scarce VFL and remains effective under heterogeneous settings.