# Secure and Efficient Federated Gradient Boosting Decision Trees
## 7 Conclusions  
With the emergence of information leakage incidents, federated learning has been paid more and more attention because of its privacy protection characteristics. The SeFB method proposed in this paper is divided into two stages: building a tree and updating the tree. In the first stage, we use LSH to calculate similar instances, and build a tree by weighted gradients. This not only solves the problem of low accuracy of the model caused by different data distribution, but also protects the safety of the original data of each participant. In the second stage, we calculate the global leaf weight that only needs one round of communication to update the tree, and compensate the accuracy loss caused by local calculation by improving the attribute mechanism of the tree. This not only ensures the accuracy of prediction, but also greatly improves the communication efficiency.  
In that discussion section of this paper, we first evaluate SeFB from three aspects: security, computational complexity and communication cost. The results show that SeFB not only protects the privacy of the original data, but also ensures the communication efficiency of the model, so it has good practical application value. Secondly, in the aspect of prediction performance, we compare SeFB with Independent, TFL and eFL-Boost through F1-score, ROC-AUC and Test errors. The experimental results show that SeFB is superior to TFL and eFL-Boost in almost all cases, and has the same performance as Non.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Schemes**} & \multicolumn{3}{c}{**F1-Score**} & \multicolumn{3}{c}{**ROC-AUC**} \\ \cline{3-8}  & & **30\%** & **60\%** & **90\%** & **30\%** & **60\%** & **90\%** \\ \hline \multirow{4}{*}{Credit 1} & Non & 0.811 & 0.835 & 0.857 & 0.968 & 0.974 & 0.980 \\  & Independent & 0.751 & 0.782 & 0.801 & 0.946 & 0.962 & 0.966 \\  & TFL & 0.791 & 0.811 & 0.836 & 0.952 & 0.965 & 0.973 \\  & eFL-Boost & 0.801 & 0.823 & 0.846 & 0.964 & 0.969 & 0.975 \\  & SeFB & 0.805 & 0.837 & 0.852 & 0.965 & 0.972 & 0.979 \\ \hline \multirow{4}{*}{Credit 2} & Non & 0.898 & 0.902 & 0.907 & 0.922 & 0.927 & 0.932 \\  & Independent & 0.894 & 0.898 & 0.903 & 0.901 & 0.909 & 0.914 \\  & TFL & 0.896 & 0.899 & 0.901 & 0.906 & 0.916 & 0.924 \\  & eFL-Boost & 0.897 & 0.901 & 0.903 & 0.910 & 0.922 & 0.927 \\  & SeFB & 0.898 & 0.901 & 0.905 & 0.919 & 0.925 & 0.931 \\ \hline \multirow{4}{*}{Adult} & Non & 0.740 & 0.751 & 0.772 & 0.888 & 0.896 & 0.903 \\  & Independent & 0.679 & 0.693 & 0.714 & 0.854 & 0.857 & 0.862 \\ \cline{1-1}  & TFL & 0.712 & 0.729 & 0.745 & 0.867 & 0.876 & 0.883 \\ \cline{1-1}  & eFL-Boost & 0.730 & 0.743 & 0.766 & 0.875 & 0.883 & 0.898 \\ \cline{1-1}  & SeFB & 0.736 & 0.749 & 0.772 & 0.885 & 0.891 & 0.902 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance impact of data volume size.  
Figure 6: Impact of data volume size on Test Errors. (**a**) Credit 1 dataset, (**b**) Credit 2 dataset, (**c**) Adult dataset.  
During the whole training process, participants need to share the gradient information of local data when updating the boosting tree, so there is still the risk of information leakage. In the future, the research will focus on the privacy security in the process of updating the federated gradient boosting decision tree model to protect the security of local model parameters.  
Conceptualization, X.Z. and S.S.; Methodology, X.J.; Writing--original draft, X.Z.; Supervision, X.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Liaoning Applied Basic Research Program, (2022 JH2/1013-00278, 2022 JH2/101300279).  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.