## 5 Experimental Evaluation
### Performance Comparison on CIFAR-10 Dataset  
Figure 12 compares the performance of various FL methods based on their accuracy. The purpose of this comparison was to highlight the effectiveness of the UA-FedRL method in comparison to existing FL techniques. The Fed_Avg method, which is based on the standard federated averaging algorithm, obtained an accuracy of 50.95%. This approach involves a weighted averaging of local model updates from participating clients to update the global model. Despite its popularity, the relatively low accuracy rate of the Fed_Avg method for non-IID data distribution on the CIFAR10 dataset reveals potential limitations in its effectiveness for non-IID datasets. The Fed_Prox method, which is an extension of the Fed_Avg algorithm, introduces a proximity term to penalize local updates that deviate significantly from the global model. This method achieved an accuracy of 60.37%, indicating a significant improvement over the Fed_Avg method. The Fed_SGD method, which employs SGD in an FL environment, yielded an accuracy of 45.98%. Although this approach is simple to implement and has been extensively studied, the results demonstrate that it may not always be the most suitable choice to achieve the highest accuracy rates in FL. The Fed_Share method, a communication-efficient approach that reduces the quantity of data exchanged between clients and the central server by sharing selected model parameters, achieved an accuracy of 43.63%. The significantly lower accuracy rate compared to the other methods indicates that the increases in communication efficiency may come at the cost of reduced model performance. However, the UA-FedRL method outperformed all other techniques by achieving an accuracy rate of 62.75%. This result demonstrates the prospective benefits of the UA-FedRL method to improve the overall performance of FL models. The increased accuracy rate demonstrates that the new approach can offer significant benefits over existing methods and pave the way for more effective applications of FL in heterogeneous industrial IoT scenarios. The acquired accuracy of the implemented models can be seen in Table 6.  
Figure 11: The accuracy comparison between UA-FedRL, Fed_AVG, and Fed_Prox methods on the MNIST dataset with 90% straggler IoT devices. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Levels** & **Fed_Avg** & **Fed_Prox** & **UA-FedRL** \\ \hline Low & 93.57\% & 94.48\% & 96.45\% \\ Medium & 83.98\% & 93.19\% & 95.76\% \\ High & 80.87\% & 91.69\% & 93.75\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The acquired accuracy of UA-FedRL, Fed_AVG, and Fed_Prox on the MNIST dataset with 90% straggler IoT devices. We further evaluated the effectiveness of the top three FL methods from Figure 13 in handling straggling devices, a common challenge in real-world scenarios where IoT devices in an FL network exhibit varying computational capacities. Figure 13 presents a comparison of three FL strategies, evaluating their accuracy on the CIFAR-10 dataset amidst challenges like 90% stragglers and different non-IID levels. The traditional Fed_Avg method, employing weighted averaging from client models for global updates, reached a maximum accuracy of 50% under low non-IID data but that accuracy decreased with higher data heterogeneity. On the other hand, Fed_Prox, an advanced version of federated averaging, achieved an accuracy of around 55%. In particular, its efficiency was also affected by the presence of high non-IID data distributions with a lower accuracy of 44%. In contrast, the UA-FedRL mechanism introduces Bayes by backprop and variational inference reinforcement learning, allowing adaptive local epoch adjustments based on individual device capabilities. Furthermore, the integration of the predictive weighted average aggregation technique enables UA-FedRL to consistently demonstrate superior accuracy, surpassing 60%.