# 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  
This group of experiments considers multiple source domains with distinct working conditions from the target domain. The fault types in the source domains are the same, but they do not overlap with the fault types in the target domain. This situation reflects a complex real-world scenario where the working conditions and fault types in the target domain differ from those in the source domains. Utilizing data from source domains with common working conditions and fault types can assist the target domain in fault diagnosis. The experimental results are presented in Table 8, and the average accuracy of all tasks is illustrated in Figure 6. Remarkably, FedCDAE-MN achieves the highest accuracy in 10 out of the 16 tasks, with an impressive average accuracy of 96%, surpassing all other methods. Moreover, compared to previous single source domain experiments, FedCDAE-MN exhibits substantial growth, elevating its average accuracy from 93% to 96%, demonstrating its exceptional adaptability and effectiveness in confronting the challenges of few-shot fault diagnosis in multi-source domain scenarios. The success of FedCDAE-MN can be attributed to its adeptness in extracting relevant prior knowledge from source domains under distinct conditions. This capacity empowers it to achieve superior generalization within the target domain, even when faced with varying operational conditions and fault types. However, it is noteworthy that the average accuracy of DS-MN and Finetune methods decreased in the multi-source domain setting. Specifically, the average accuracy of DS-MN decreased from 88% to 82%, indicating that the performance of DS-MN may be \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Load 012\(\rightarrow\)3** & **Load 013\(\rightarrow\)2** & **Load 023\(\rightarrow\)1** & **Load 123\(\rightarrow\)0** & **AVG** \\ \hline DS-MN & 0.85 & 0.89 & 0.90 & 0.84 & 0.87 \\ Finetune & 0.89 & 0.99 & **0.99** & 0.98 & 0.96 \\ FS-MN & **0.98** & 1.00 & 0.98 & 0.97 & 0.98 \\ FeatureKNN1 & 0.72 & 0.87 & 0.87 & 0.80 & 0.81 \\ FeatureKNN2 & 0.86 & 0.98 & 0.92 & 0.91 & 0.92 \\ FedCAE-MN & 0.95 & 1.00 & 0.97 & 0.97 & 0.97 \\ FedCDAE-MN & 0.97 & **1.00** & 0.97 & **0.99** & **0.98** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Average accuracy of the DCSF experiment. influenced by domain shift resulting from the increase in the number of source domains. These findings contribute to our understanding of few-shot fault diagnosis in complex multi-source domain environments, paving the way for further research into optimizing methods for enhanced adaptability and robustness. In summary, our experimental results underscore the exceptional performance of FedCDAE-MN in multi-source domain fault-diagnosis scenarios. Its ability to leverage knowledge from various source domains under different operating conditions demonstrates its adaptability and effectiveness. Furthermore, four transferring experiments were conducted on the Gearbox dataset for the DCDF scenario with two tasks. In this setting, CT represents the target domain fault types as normal state and clipped tooth, and the source domain fault types as normal