# Communication-Efficient Vertical Federated Learning
## 5 Results
### Performance using Autoencoders
Overfitting is possible due to the small number of samples in the _Heart Disease Dataset_. The losses of the autoencoders across all clients are very low in the case of the _Wine Quality Dataset_, indicating that the autoencoders can compress the raw local data with max \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Accuracy (\%)**} & \multicolumn{3}{c}{**F1 Score (\%)**} \\ \hline Centralized Learning & \multicolumn{3}{c}{86.81} & \multicolumn{3}{c}{86.21} \\ Guest Party Performance & \multicolumn{3}{c}{81.32} & \multicolumn{3}{c}{81.10} \\ \hline \multicolumn{5}{c}{Autoencoder Performance} \\ \multicolumn{5}{c}{Compression 25\%} & \multicolumn{3}{c}{Compression 50\%} & \multicolumn{3}{c}{Compression 75\%} \\ Training Loss & Validation Loss & Training Loss & Validation Loss & Training Loss & Validation Loss \\ \hline C1 & 15.78 & 15.46 & 23.60 & 22.12 & 33.05 & 32.15 \\ C2 & 7.98 & 7.98 & 16.13 & 15.16 & 19.98 & 21.12 \\ C3 & 5.87 & 5.46 & 8.87 & 7.72 & 10.25 & 9.98 \\ \hline \multicolumn{5}{c}{Performance on Aggregated Latent Data} \\ \multicolumn{5}{c}{Compression 25\%} & \multicolumn{3}{c}{Compression 50\%} & \multicolumn{3}{c}{Compression 75\%} \\ Accuracy (\%) & F1 Score (\%) & Accuracy (\%) & F1 Score (\%) & Accuracy (\%) & F1 Score (\%) \\ \hline Encoding C1 & 78.76 & 78.77 & 78.17 & 78.19 & 75.29 & 75.31 \\ Without Encoding C1 & 81.84 & 81.84 & 80.98 & 80.99 & 80.74 & 80.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Experimental Results on Heart Disease Prediction Dataset. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Accuracy (\%)**} & \multicolumn{3}{c}{**F1 Score (\%)**} \\ \hline Centralized Learning & \multicolumn{3}{c}{75.10} & \multicolumn{3}{c}{74.29} \\ Guest Party Performance & \multicolumn{3}{c}{66.10} & \multicolumn{3}{c}{58.95} \\ \hline \multicolumn{5}{c}{Autoencoder Performance} \\ \multicolumn{5}{c}{Compression 25\%} & \multicolumn{3}{c}{Compression 50\%} & \multicolumn{3}{c}{Compression 75\%} \\ Training Loss & Validation Loss & Training Loss & Validation Loss & Training Loss & Validation Loss \\ \hline C1 & 0.2049 & 0.2709 & 0.2157 & 0.2387 & 0.3624 & 0.3819 \\ C2 & 0.5218 & 0.5338 & 0.6073 & 0.6169 & 0.8411 & 0.8732 \\ C3 & 0.2902 & 0.3007 & 0.3034 & 0.3407 & 0.5148 & 0.5332 \\ \hline \multicolumn{5}{c}{Performance on Aggregated Latent Data} \\ \multicolumn{5}{c}{Compression 25\%} & \multicolumn{3}{c}{Compression 50\%} & \multicolumn{3}{c}{Compression 75\%} \\ Accuracy (\%) & F1 Score (\%) & Accuracy (\%) & F1 Score (\%) & Accuracy (\%) & F1 Score (\%) \\ \hline Encoding C1 & 78.43 & 78.45 & 78.10 & 78.15 & 75.36 & 75.39 \\ Without Encoding C1 & 81.69 & 81.70 & 81.29 & 81.30 & 81.24 & 81.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Experimental Results on Wine Quality Dataset. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Accuracy (\%)**} & \multicolumn{3}{c}{**F1 Score (\%)**} \\ \hline Centralized Learning & \multicolumn{3}{c}{88.20} & \multicolumn{3}{c}{88.17} \\ Guest Party Performance & \multicolumn{3}{c}{73.83} & \multicolumn{3}{c}{73.40} \\ \hline \multicolumn{5}{c}{Autoencoder Performance} \\ \multicolumn{5}{c}{Compression 25\%} & \multicolumn{3}{c}{Compression 50\%} & \multicolumn{3}{c}{Compression 75\%} \\ Training Loss & Validation Loss & Training Loss & Validation Loss & Training Loss & Validation Loss \\ \hline C1 & 61.46 & 62.98 & 77.52 & 79.43 & 93.41 & 96.24 \\ C2 & 42.39 & 43.73 & 61.48 & 63.68 & 78.66 & 79.91 \\ C3 & 18.21 & 19.18 & 21.57 & 22.05 & 31.06 & 34.37 \\ \hline \multicolumn{5}{c}{Performance on Aggregated Latent Data} \\ \multicolumn{5}{c}{Compression 25\%} & \multicolumn{3}{c}{Compression 50\%} & \multicolumn{3}{c}{Compression 75\%} \\ Accuracy (\%) & F1 Score (\%) & Accuracy (\%) & F1 Score (\%) & Accuracy (\%) & F1 Score (\%) \\ \hline Encoding C1 & 78.57 & 78.80 & 71.78 & 71.42 & 71.60 & 70.89 \\ Without Encoding C1 & 74.23 & 73.90 & 74.17 & 73.85 & 74.02 & 73.79 \\ \hline \hline \end{tabular}
\end{table}