Toward Few-Label Vertical Federated Learning

作者：Lei Zhang, Lele Fu, Chen Liu, Yang Zhao, Jing‐Hua Yang, Zibin Zheng, Chuan Chen · 发表于：ACM Transactions on Knowledge Discovery from Data · 年份：2024 · DOI：10.1145/3656344 · 被引用次数：12 · 研究领域：Privacy-Preserving Technologies in Data、Internet Traffic Analysis and Secure E-voting、Mobile Crowdsensing and Crowdsourcing

Federated Learning (FL) provides a novel paradigm for privacy-preserving machine learning, enabling multiple clients to collaborate on model training without sharing private data. To handle multi-source heterogeneous data, Vertical Federated Learning (VFL) has been extensively investigated. However, in the context of VFL, the label information tends to be kept in one authoritative client and is very limited. This poses two challenges for model training in the VFL scenario. On the one hand, a small number of labels cannot guarantee to train a well VFL model with informative network parameters, resulting in unclear boundaries for classification decisions. On the other hand, the large amount of unlabeled data is dominant and should not be discounted, and it is worthwhile to focus on how to leverage them to improve representation modeling capabilities. To address the preceding two challenges, we first introduce supervised contrastive loss to enhance the intra-class aggregation and inter-class estrangement, which is to deeply explore label information and improve the effectiveness of downstream classification tasks. Then, for unlabeled data, we introduce a pseudo-label-guided consistency mechanism to induce the classification results coherent across clients, which allows the representations learned by local networks to absorb the knowledge from other clients, and alleviates the disagreement between different clients for classification tasks. We conduct sufficient experiments on fo...