# Privacy-Preserving Handover Optimization Using Federated Learning and LSTM Networks
## 4 Results
### Dynamic Handover Algorithm Performance  
Table 3 presents the model prediction accuracy across different datasets and learning methods, highlighting the exceptional performance of the dynamic algorithm. This study employs three different datasets--HSPA+/LTE/5G, LTE/5G, and 5G--to simulate various network conditions and evaluate the performance of different learning methods: Heterogeneous Federated Learning (Hete_FL), Homogeneous Federated Learning (Homo_FL), and a Centralized LSTM Model (Cen_LSTM). The results underscore the significant success of the dynamic algorithm in adapting to these diverse conditions. Heterogeneous Federated Learning (Hete_FL) achieved the highest accuracy across all datasets, especially in the 5G dataset with an accuracy of 0.9460. This indicates that \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **MAE** & **MSE** & **RMSE** \\ \hline Hete\_FL 1 & 3.5021 & 5.2394 & 2.2883 \\ Homo\_FL 2 & 3.3728 & 4.6171 & 2.1476 \\ Cen\_LSTM 3 & 3.0356 & 18.9824 & 4.3529 \\ \hline \hline \end{tabular}  
* Heterogeneous federated learning. 2 Homogeneous federated learning. 3 Centralized LSTM Model.  
\end{table} 
Table 2: Model prediction performance comparison. (Hete_FL) is highly effective in handling diverse and heterogeneous data sources, which is essential in real-world network environments where data can vary significantly. Homogeneous Federated Learning (Homo_FL) also demonstrated strong performance, particularly in the 5G dataset with an accuracy of 0.9901. This method excels in environments where data sources are more uniform, showcasing its robustness and reliability in such scenarios. The centralized LSTM Model (Cen_LSTM), while slightly lower in accuracy compared to the federated learning approaches, still delivered impressive results. It performed particularly well in the LTE/5G dataset with an accuracy of 0.9647, highlighting its capability to maintain high performance in less heterogeneous conditions. As shown in the Table 3, Hete_FL and Homo_FL demonstrated similarly high performance in all tested network environments, including HSPA+/LTE/5G, LTE/5G, and 5G. The minimal differences between the two methods indicate that both are robust and effective in handling different network conditions. The slight advantage of Hete_FL in heterogeneous environments may be attributed to its ability to better manage changes in profile distribution. However, the small performance gap shows that Homo_FL can adapt well even in cases where the data distribution may not be completely uniform. This similarity in performance highlights the flexibility and advantages of federated learning, regardless of whether the data environment is more homogeneous or heterogeneous. The centralized LSTM model, while still effective, exhibits slightly lower performance, especially in more advanced network scenarios such as 5G, possibly due to its centralized nature, which may limit its ability to capture the inherent limitations of distributed data sources and diversity capabilities. In Figure 10 presents the 95% confidence intervals (CI) for three key performance indicators--MAE, MSE, and RMSE--across three models: Hete_FL, Homo_FL, and Cen_LSTM. The results clearly demonstrate that Hete_FL outperforms the other models on all metrics, exhibiting the lowest MAE, MSE, and RMSE, which indicates superior accuracy and reduced error dispersion. The tight confidence intervals for Hete_FL further enhance the credibility of these findings, showcasing the model's reliability and consistency in predicting RSRP values. In contrast, Cen_LSTM displays wider confidence intervals, reflecting greater variability and lower prediction accuracy, which underscores the advantages of federated learning methods, particularly in heterogeneous environments. The use of confidence intervals in this analysis highlights the stability and practical effectiveness of the Hete_FL model proposed in this study. In Figure 11 illustrates the Handover Probability (HOPP) for three different algorithms: Dynamic, A3_Event, and Greedy.