# Distributed Online Multi-Label Learning with Privacy Protection in Internet of Things
## 7 Results
### Performance Analysis in a Large-Scale Distributed Environment
#### 7.2.2 F1 Score Evaluation  
Tables 7 and 8 present the DOML algorithm's F1 scores for a 20-node network and 100-node network, respectively, with varying degrees of network connectivity. Figures 6 and 7 compare the performance of the macro f1 score and the micro f1 score under different network conditions, respectively. In the experiment of F1 scores, we can observe a large variance in the DOML results. It is especially more obvious in larger networks. This is due to the differentiation caused by a large number of nodes. We use the middle value of the maximum and minimum values as a reference to consider the F1 score performance of all nodes in the DOML algorithm. \begin{table}
\begin{tabular}{l c c c c} \hline  & \multicolumn{3}{c}{**DOML**} & **OML** \\ \cline{2-5}  & **25\%** & **50\%** & **100\%** & **-** \\ \hline CAL500 & 0.1954 \(\pm\) 0.0450 & 0.1954 \(\pm\) 0.0450 & 0.1954 \(\pm\) 0.0450 & **0.1499** \\ Corel5k & 0.0145 \(\pm\) 0.0051 & 0.0147 \(\pm\) 0.0051 & 0.0145 \(\pm\) 0.0049 & **0.0124** \\ Emotions & **0.4422 \(\pm\) 0.1048** & **0.4422 \(\pm\) 0.1048** & **0.4422 \(\pm\) 0.1048** & 0.4695 \\ Enron & 0.1027 \(\pm\) 0.0380 & 0.0955 \(\pm\) 0.0311 & 0.0881 \(\pm\) 0.0239 & **0.0665** \\ Medical & 0.1133 \(\pm\) 0.0833 & 0.1133 \(\pm\) 0.0833 & 0.1133 \(\pm\) 0.0833 & **0.0300** \\ scene & **0.2656 \(\pm\) 0.0846** & 0.2667 \(\pm\) 0.0857 & 0.2743 \(\pm\) 0.0933 & 0.2919 \\ yeast & 0.2094 \(\pm\) 0.0751 & 0.2009 \(\pm\) 0.0673 & 0.2099 \(\pm\) 0.0756 & **0.1310** \\ \hline \end{tabular} The numbers in bold highlight the best performing of the three algorithms.  
\end{table} 
Table 6: Hamming loss of DOML in a 100-node network with different connectivity and OML with same amount of instances as each node. Figure 5: Hamming loss of DOML in a 100-node network with different connectivity and OML with same amount of instances as each node. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Macro F1 Score**} & \multicolumn{4}{c}{**Micro F1 Score**} \\ \cline{2-9}  & \multicolumn{2}{c}{**DOML**} & \multicolumn{2}{c}{**OML**} & \multicolumn{2}{c}{**DOML**} & \multicolumn{2}{c}{**OML**} \\ \cline{2-9}  & **25\%** & **50\%** & **100\%** & **-** & **25\%** & **50\%** & **100\%** & **-** \\ \hline CAL500 & **0.0672** & **0.0672** & **0.0672** & 0.0369 & **0.3173** & **0.3173** & **0.3173** & 0.2746 \\ Corel5k & 0.0016 & **0.0020** & 0.0017 & 0.0009 & 0.0798 & **0.0862** & 0.0791 & 0.0382 \\ Emotions & **0.1708** & **0.1708** & **0.1708** & **0.1497** & **0.3167** & **0.3167** & **0.3167** & 0.2914 \\ Enron & 0.0232 & 0.0214 & 0.0211 & **0.0362** & 0.2312 & 0.2217 & 0.2055 & **0.4457** \\ Medical & **0.0122** & **0.0122** & **0.0122** & 0.0000 & **0.0670** & **0.0670** & **0.0670** & 0.0000 \\ scene & 0.0577 & 0.0582 & **0.0585** & 0.0478 & 0.1275 & 0.1295 & 0.1299 & **0.1603** \\ yeast & 0.2357 & 0.2483 & 0.2154 & **0.2862** & 0.5381 & 0.5718 & 0.4908 & **0.7894** \\ \hline \hline \end{tabular} The numbers in bold highlight the best performing of the three algorithms.  
\end{table} 
Table 8: F1 Score of DOML in a 100-node network with different connectivity and OML with the same amount of instances as each node. Figure 6: Macro F1 Score of DOML in a 100-node network with different connectivity and OML with same amount of instances as each node.