# LSH Models in Federated Recommendation
## 5 Conclusions  
In this paper, we have explored the application of federated learning to the neural collaborative filtering (NCF) recommendation model. Our study began by comparing \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Dim = 8**} & \multicolumn{3}{c}{**Dim = 64**} \\ \cline{2-7}  & **HR** & **NDCG** & **F1** & **HR** & **NDCG** & **F1** \\ \hline GMF & 0.4128 & 0.2387 & 0.3001 & 0.4729 & 0.2800 & 0.3549 \\ \hline MLP & 0.3562 & 0.2211 & 0.2829 & 0.4699 & 0.2705 & 0.3526 \\ \hline NeuMF & 0.3721 & 0.2397 & 0.3052 & 0.5123 & 0.2997 & 0.4064 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: NCF federated recommendation with LSH. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Dim = 64**} & \multicolumn{3}{c}{**\(\varepsilon\) = 60**} & \multicolumn{3}{c}{**Increase/Decrease**} \\ \cline{2-9}  & **HR** & **NDCG** & **F1** & **HR** & **NDCG** & **F1** & \\ \hline GMF & 0.4729 & 0.2800 & 0.3549 & 0.4401 & 0.2411 & 0.3221 & HR increased by 0.0318, NDCG decreased by 0.0389, and F1 increased by 0.0328 \\ \hline MLP & 0.4699 & 0.2705 & 0.3526 & 0.4489 & 0.2622 & 0.3797 & HR increased by 0.0210, NDCG increased by 0.0083, and F1 decreased by 0.0271 \\ \hline NeuMF & 0.5123 & 0.2997 & 0.4064 & 0.4837 & 0.2590 & 0.3680 & HR increased by 0.0286, NDCG by 0.0407, and F1 by 0.0384 \\ \hline \hline \end{tabular} Note: The increase/decrease column shows the change in HR, NDCG, and F1 scores at LSH (dim = 64) compared to DP (\(\varepsilon\) = 60). Positive values indicate that the LSH method outperformed the DP method on this index, and negative values indicate a decrease. It can be seen from the table that for the GMF and NeuMF models, the LSH method improves compared to DP in HR and F1 scores, but the GMF model decreased while the NeuMF model improved in NDCG. For the MLP model, the LSH method improved in HR, while it was close to the DP method, or even decreased slightly, in the NDCG and F1 scores.  
\end{table} 
Table 7: Comparison of LSH and DP. the federated recommendation model based on NCF with traditional recommendation systems, highlighting the trade-offs between privacy and recommendation accuracy. We then examined the impact of participant numbers on federated learning and demonstrated the benefits of pre-training, particularly with the NeuMF model. Secondly, we proposed a model weight setting scheme based on participation levels and investigated the effects of different weight-setting functions on the federated learning process. Thirdly, we introduced a local parameter NCF recommendation scheme to study the influence of embedding layer local parameters on federated recommendations. Fourthly, we proposed an NCF federated recommendation model with a differential privacy (DP) scheme to enhance model privacy, achieving a balance between privacy and recommendation effectiveness through parameter tuning. Our fifth contribution involved the introduction of locality sensitive hashing (LSH) to the NCF collaborative recommendation model, aiming to improve privacy protection. By adjusting the dimensionality of hash vectors, we were able to enhance the system's performance. In summary, our research combines pre-training, local parameters, DP, and LSH techniques to improve federated learning in recommender systems. These enhancements have not only improved the federated learning process, efficiency, and recommendation accuracy but also bolstered data privacy and protected client data. Comparative experiments have validated the proposed models and assessed their recommendation effectiveness and privacy protection capabilities. Despite the progress made, there are several limitations to this study that warrant further investigation:  
1. The model weight setting function is linear and may not capture the complexity of real-world user participation patterns. Non-linear or adaptive functions could be explored in future work. 2.