Tree boosting is a widely used machine learning model in many financial fields. Additively homomorphic encryption is an important cryptographic tool used for secure tree boosting in the setting of federated learning. However, homomorphic encryption includes computationally expensive operations. Current frameworks for secure tree boosting are extremely slow. In this article, we propose E-Booster, a novel accelerator for the training of secure tree boosting. E-Booster can fully exploit algorithmic superiority and architectural optimization to achieve unprecedented performance, and to address the obstacle in deploying additively homomorphic encryption in industrial applications. E-Booster has been implemented on an Intel Agilex field-programmable gate array and evaluated on four public datasets. It achieves a 5.1–7.8-times speedup over a CPU with 32 threads for secure tree boosting. To the best of our knowledge, E-Booster is the first additively homomorphic encryption accelerator that can be applied to industrial secure tree boosting.