A Vertical Federated Learning Framework for Horizontally Partitioned Labels

Understand

Vertical federated learning is a collaborative machine learning framework to train deep leaning models on vertically partitioned data with privacy-preservation.

• It attracts much attention both from academia and industry.
• Unfortunately, applying most existing vertical federated learning methods in real-world applications still faces two daunting challenges.
• First, most existing vertical federated learning methods have a strong assumption that at least one party holds the complete set of labels of all data samples, while this assumption is not satisfied in many practical scenarios, where labels are horizontally partitioned and the parties only hold partial labels.