Federated learning (FL) enables independent parties to collaboratively build machine learning (ML) models while protecting data privacy. Vertical federated learning (VFL), a variant of FL, has recently drawn increasing attention as the VFL matches the enterprises' demands of leveraging more valuable features to achieve better model performance without jeopardizing data privacy. However, conventional VFL may run into data deficiency as it is only able to exploit aligned samples (belonging to different parties) with labels, leaving often the majority of unaligned and unlabeled samples unused. The data deficiency hampers the effort of the federation. In this work, we propose a Federated Hybrid Self-Supervised Learning framework, coined FedHSSL, to utilize all available data (including unaligned and unlabeled samples) of participants to train the joint VFL model. The core idea of FedHSSL is to utilize cross-party views (i.e., dispersed features) of samples aligned among parties and local views (i.e., augmentations) of samples within each party to improve the representation learning capability of the joint VFL model through SSL (e.g., SimSiam). FedHSSL further exploits generic features shared among parties to boost the performance of the joint model through partial model aggregation. We empirically demonstrate that our FedHSSL achieves significant performance gains compared with baseline methods, especially when the number of labeled samples is small. We provide an in-depth analysis of FedHSSL regarding privacy leakage, which is rarely discussed in existing self-supervised VFL works. We investigate the protection mechanism for FedHSSL. The results show our protection can thwart the state-of-the-art label inference attack.

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