DPTB-VFL: An Efficient Vertical Federated Learning Framework Based on Boosting Trees and Adaptive Differential Privacy

Vertical Federated Learning (VFL) offers a promising approach to collaborative model training, allowing participants to share identical data samples with distinct attributes. This approach avoids direct raw data sharing, enhancing data privacy, though it may sometimes reduce model accuracy. In this paper, we introduce DPTB-VFL, a differential privacy-based vertical federated learning framework that leverages boosting trees. Boosting trees are chosen for their exceptional ability to handle heterogeneous features, deliver robust performance with minimal preprocessing, and provide interpretability-all crucial in privacy-sensitive scenarios. Additionally, most current methods apply a uniform privacy budget across all training steps, overlooking variations in local gradients that could better balance privacy and utility. DPTB-VFL addresses this gap by introducing an adaptive differential privacy protocol, enabling dynamic privacy budget allocation based on the model's learning progress. Experimental evaluations on three public datasets demonstrate that DPTB-VFL not only achieves higher accuracy than existing approaches but also significantly reduces computational latency, aligning data privacy needs with model performance