# Federated Few-Shot Learning-Based Machinery Fault Diagnosis in the Industrial Internet of Things
## 5 Experimental Study
### Analysis of Experimental Results
#### 5.5.3 DCDF Experiments
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{Load 012\(\rightarrow\)3} & \multicolumn{4}{c}{Load 013\(\rightarrow\)2} \\ \hline  & WIOB & B & I & O & WIOB & B & I & O \\ \hline DS-MN & 0.98 & 0.74 & 0.87 & 0.87 & 0.86 & 0.67 & 0.78 & 0.61 \\ Finetune & 0.96 & 0.90 & 0.92 & 0.73 & 1.00 & 0.77 & 0.90 & 0.74 \\ FS-MN & 0.99 & 0.88 & 1.00 & 0.94 & 1.00 & **0.97** & **0.99** & 0.87 \\ FeatureKNN1 & 0.97 & 0.95 & 0.93 & 0.81 & 0.87 & 0.86 & 0.97 & 0.80 \\ FeatureKNN2 & 0.99 & 0.99 & 0.86 & 0.91 & 0.99 & 0.87 & 0.94 & 0.83 \\ FedCAE-MN & **1.00** & 0.99 & 1.00 & **0.99** & 1.00 & 0.96 & 0.97 & 1.00 \\ FedCDAE-MN & 0.98 & **1.00** & **1.00** & 0.93 & **1.00** & 0.92 & 0.95 & **1.00** \\ \hline  & \multicolumn{4}{c}{Load 023\(\rightarrow\)1} & \multicolumn{4}{c}{Load 123\(\rightarrow\)0} \\ \hline  & WIOB & B & I & O & WIOB & B & I & O \\ \hline DS-MN & 0.99 & 0.70 & 0.93 & 0.83 & 0.80 & 0.78 & 0.84 & 0.86 \\ Finetune & 1.00 & 0.68 & 0.90 & 0.86 & 0.95 & 0.68 & 0.90 & 0.86 \\ FS-MN & 0.93 & 0.77 & 0.97 & 0.83 & 0.97 & 0.80 & 1.00 & 0.88 \\ FeatureKNN1 & 0.77 & 0.72 & 0.95 & 0.79 & 0.98 & 0.69 & 0.97 & 0.80 \\ FeatureKNN2 & 0.99 & 0.78 & 0.96 & 0.92 & 0.98 & 0.71 & 0.94 & 0.89 \\ FedCAE-MN & **1.00** & 0.77 & 0.97 & 0.66 & 0.99 & **0.88** & 1.00 & 0.97 \\ FedCDAE-MN & 0.99 & **0.82** & **1.00** & **1.00** & **1.00** & 0.82 & **1.00** & **0.97** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Average accuracy of the DCDF experiment. Figure 6: Overall accuracy of all methods. state and missing tooth. On the other hand, MT represents the target domain fault types as normal state and missing tooth, and the source domain fault types as normal state and clipped tooth. The experimental results are presented in Table 9, and the average accuracy of all tasks is displayed in Figure 7. Notably, FS-MN and Finetune methods only achieved an accuracy of approximately 50% on specific tasks, which is comparatively lower than that of other methods. This further emphasizes the inherent limitations of these two methods in multi-source domain scenarios, including potential issues related to domain shift, feature extraction, and generalization. Conversely, the remaining methods show remarkable resilience and effectiveness in this context, maintaining average accuracy levels consistently above 95%. This performance demonstrates their capacity to navigate the complexities of multi-source domain scenarios adeptly. It is worth noting that FedCDAE-MN and FeatureKNN2 achieves the highest accuracy of 97%. This achievement further highlights the power of convolutional denoising auto-encoders in feature extraction and domain generalization. In conclusion, the extensive examination of our experimental results sheds light on the performance disparities among various fault-diagnosis methods within the multi-source domain context. The limitations observed for FS-MN and Finetune methods underscore the formidable hurdles that need to be overcome in adapting to this environment. Conversely, the performance of FedCDAE-MN and FeatureKNN2 show the significance of advanced feature extraction techniques, particularly convolutional denoising auto-encoders, in enhancing domain generalization and diagnosis accuracy.