## 4 Experiment and Analysis
### Results and Discussion  
In this sub-section, the results of precision, accuracy/loss of IIoT-EFL-based imaging services, and QoS performances of E2E IIoT networks are given to allow comparison between the proposed MLSTM-CEFL, MLSTM-CCFL, and CLSTM-CFL. The percentage of training and testing accuracies are given for MLSTM-based modules in predicting the congestion rates at every input time slot. Figure 4 illustrates the accuracy and loss value of the proposed and baseline approaches. Within 125 sampling time slots, the prediction rates from non-congestion to a heavy-congestion level are acceptable, illustrated by an insignificant difference between them. With precise congestion rate prediction, the proposed orchestrator and controller significantly improve the proactive resource placement towards the optimal edge aggregator for particular conditional intervals. In terms of (training, testing), the proposed module achieves (4.1979%, 7.2369%) and (10.6698%, 13.9758%) higher accuracies than MLSTM-CCFL and CLSTM-CFL, respectively. MLSTM-CCFL fails to focus on the distinct edge-specific service that could contribute to more accurate centralized congestion predictions. Accuracy is a metric that is primarily used for classification problems where the goal is to correctly classify data points into different classes. In the context of time series prediction (multi-round communication in EFL) with MLSTM, accuracy acts as a certain case in predicting the exact congestion rate values. In our simulation setup, we convert the congestion rate predictions into three discrete classes (low, normal, and high congestion) and then calculate accuracy based on those classes. Our approach transforms the regression problem into a classification problem by dividing it into a limited number of classes for prediction. We delineate congestion rate ranges: 0-30% as low congestion, 30-70% as normal congestion, and above 70% as high congestion. Subsequently, we map the predicted congestion rate to one of these classes and calculate accuracy based on how effectively the model categorizes the congestion rates into the correct states. Another primary focus is the loss metric (MSE); the goal is to minimize it to ensure accurate predictions of congestion rates at the aggregator buffers over multi-round communications. The loss represents the discrepancy between the predicted values and the actual target values. In our setup, the loss value measures how well the model is able to predict the congestion rate at the edge for a given time step. MSE measures the average squared difference between the predicted values and the actual target values, and the lower the MSE, the better the model is at fitting the training data. Table 4 presents each approach output in terms of accuracy and loss values. After installing the forwarding path and orchestrating the virtual edge resources by following the congestion thread notifications from the proposed MLSTM-CP module, the FL simulation on the imaging dataset is conducted to represent each policy rule. With appropriate offloading of decision makers, the number of round communications is alleviated and the drop possibility of local distribution is significantly reduced. The training and testing loss values of MLSTM-CEFL reach 0.0225 and 0.0399, respectively. Figure 5 presents the primary QoS metrics captured for evaluation in this simulation setup. The accuracy and reliability of predicting the congestion rate at an edge aggregator outputs good results using MLSTM-CEFL. The predicted rate and actual experiment rate configuration are acceptable; the policy created a model that can effectively forecast the congestion rate at the edge to help in managing network resources efficiently. A higher similarity in Figure 5a illustrates a predicting congestion rate that allows FL central policy administrators to (1) proactively allocate resources, (2) balance loads, and (3) prevent potential network congestion.