VFBoost-MO: Scable vertical federated gradient boosting decision tree for multi-class problem

As a key technology to protect data privacy, vertical federated learning has recently been developed fast in both academia and industry. Notable works such as secureboost and secureboost+, which are all additive ensemble models that use a gradient boosting tree have been widely used in many applications. This paper proposes VFBoost-MO, a novel vertical federated learning (VFL) framework for multi-class classification using gradient boosting decision trees (GBDTs). Existing VFL methods for GBDTs are primarily limited to binary classification, while multi-class methods suffer from high computational and communication complexity. VFBoost-MO addresses these challenges by introducing a class-pair selection algorithm that constructs a single vector tree per iteration, significantly reducing complexity. Additionally, the framework employs local differential privacy (LDP) to protect data privacy without relying on computationally expensive encryption techniques. We present a theoretical analysis and experimental evaluation on eight public datasets demonstrating that VFBoost-MO achieves comparable accuracy to state-of-the-art methods while offering 2-3x performance improvement in training speed and convergence rate.