Abstract:

This study proposed an efficient and secure federated learning framework based on gradient boosting decision tree. It was used to protect privacy within a vertical federated learning environment and was capable of handling situations where feature scales were completely different or where binary and continuous features coexisted. This framework employed LightGBM as the boosting decision tree and used a symmetric encryption mechanism to safeguard gradient privacy. Security analysis indicated that it did not disclose gradient privacy and was effective in defending against the risks of data eavesdropping or tampering during transmission. Experiments conducted on biomedical datasets and commonly used credit prediction datasets validated the effectiveness of this framework, demonstrating higher efficiency than other existing gradient boosting decision tree based federated learning frameworks while maintaining the same level of accuracy.

Key words: federated learning, gradient boosting decision tree, symmetric encryption