## 5 Experimental Evaluation
### Performance Comparison on MNIST Dataset  
Figure 10 shows a comparison of the performance of the UA-FedRL method with a number of existing FL methods. This comparison assessed the precision achieved after 100 rounds of training, with 100 clients selected for all FL methods. All models were trained on non-IID data, which are commonly encountered in real-world scenarios. As shown in Figure 10, the Fed_AVG, Fed_Prox, and UAFedRL methods provided superior results compared to the Fed SGD and Fed Share approaches. The FedProx method, specifically designed to address heterogeneity and non-IID data in IoT contexts, demonstrated its effectiveness in the graph. On the contrary, the UA-FedRL method employs Bayesian techniques to tackle heterogeneity in IoT devices and leverages predictive weighted aggregation to enhance robustness when learning from non-IID data. By achieving an accuracy of 96.45%, the UA-FedRL method outperformed existing FL approaches for IoT devices. This result suggests that the UA-FedRL model is well suited for heterogeneous industrial IoT device scenarios, ensuring more robust learning capabilities  
Figure 9: The accumulated rewards of the UA-FedRL for different gamma values when the learning rate was set to 0.9.  
in real-world applications. The detailed accuracy of all FL methods used in this experiment is shown in Table 4.  
The three best FL methods from Figure 10 were further evaluated to demonstrate their effectiveness in handling straggling devices with different non-IID data distributions. In real-world scenarios, IoT devices in an FL network may possess varying computational capacities. Consequently, it is essential to conduct experiments simulating these conditions to identify suitable methods that accommodate heterogeneous industrial IoT devices. Figure 11 describes the performance metrics of three methodologies tested across 100 clients, 90% of whom were identified as stragglers. These methods were trained for 100 rounds and assessed various non-IID data distributions. Figure 11a provides an assessment of the Fed_Avg methodology under three distinct non-IID conditions: low, medium, and high. The observation suggests that while Fed_Avg showcased proficient accuracy rates, surpassing 90% under low non-IID data conditions, its efficiency notably decreased with higher levels of non-IID data distribution. This pattern underlined Fed_Avg's limited adaptability to higher heterogeneity in data and training environments. In contrast, both the Fed_Prox and UA-FedRL methodologies presented robust performance metrics under varying non-IID conditions, as depicted in Figure 11b,c, respectively. Both methodologies surpassed the 90% accuracy benchmark on various non-IID data spectrums. The architectural premise of the Fed_Prox method is calibrated to accommodate the inherent heterogeneity of IoT devices, incorporating a proximal component to counterbalance disparities during global model updates. The UA-FedRL algorithm adopts an innovative approach by integrating Bayes by backprop coupled with variational inference reinforcement learning. This fusion facilitates dynamic adjustments to local epochs, depending upon individual device computational capabilities. The introduction of the predictive weighted average aggregation method \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Algorithm** & **Accuracy** \\ \hline Fed\_AVG & Average aggregation & 94.56\% \\ Fed\_Prox & Proximal term & 94.62\% \\ Fed\_SGD & Stochastic Gradient Descent & 81.88\% \\ Fed\_Share & Data share strategy & 83.81\% \\ UA-FedRL & UA-RL + PWA & 96.45\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The acquired accuracy of UA-FEDRL and different FL methods for the MNIST dataset. Figure 10: The accuracy comparison between UA-FedRL and different FL methods on the MNIST dataset. thereby enhances the cumulative accuracy coefficient of the UA-FedRL framework. UA-FedRL achieved an accuracy of 96.45%, surpassing its FL counterparts.