## 4 Experiment and Analysis
### Results and Discussion
The MLSTM-CEFL framework, collaboratively trained across multiple edge aggregators while preserving data privacy and featuring accurate congestion \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **CLSTM-CFL** & **MLSTM-CCFL** & **MLSTM-CEFL** \\ \hline
**Training accuracy** & 88.8403\% & 95.3122\% & 99.5101\% \\
**Training loss** & 0.5991 & 0.2892 & 0.0225 \\
**Testing accuracy** & 85.1598\% & 91.8991\% & 99.1356\% \\
**Testing loss** & 0.7812 & 0.4515 & 0.0399 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Losses and accuracies of proposed schemes. Figure 4: (**a**) Accuracy and (**b**) loss value of proposed and reference policy controllers. rate predictions, results in a more effective converged final model. Delays \(T_{TOTAL}\) are captured as Equation (13) and illustrated in Figure 5b, serving as a major evaluation metric to determine the performance stability of the proposed MLSTM-CEFL, MLSTM-CCFL, and CLSTM-CFL models. In our architecture, \(t_{RAN}\), \(t_{EDGE}\), and \(t_{CONTROL}\) are jointly considered in the Mininet platform. The average delays of the proposed policy reach 30.81 ms among all service settings, which is a 46.8961 ms and 111.2661 ms improvement (lower delays) on the MLSTM-CCFL and CLSTM-CFL policies, respectively. Through the convergence of MLSTM-CP modules with system orchestrators, resources are effectively allocated in each iteration of the local-aggregator updates. Our proposed scheme ensures the system's ability to reliably serve each IIoT application class, with particular emphasis on the mission-critical service class. Accurate congestion rate prediction facilitates the well-adjusted update scheduling of local model parameters, which is particularly applicable in asynchronous FL. Given stable delay variation, experience-based forwarding is efficient for both reactive and proactive configurations. The proposed approach contributes to lower and more stable latencies, enabling the final learning model to achieve latency-efficient characteristics while preventing local models from exceeding the upper-bound maximum delays. The FL model drop ratio is presented in Figure 5c. This measure indicates the proportion of local/edge models that are dropped or discarded during the FL process due to various reasons such as (1) communication errors, (2) failures, (3) model convergence issues, or (4) insufficiency of policy orchestration. High model drop ratios can be problematic, as this indicates inefficient communication and collaboration between local devices and the aggregator. High ratios lead to delays in model updates, suboptimal performance, and increased training costs. To maintain the final model performance, our proposed scheme achieved the minimization of model drop ratios to an average of 0.376% among various congestion states in our 250 s simulation. This output provides three primary benefits: (1) enhancing communication reliability, (2) resolving convergence issues, and (3) handling  
Figure 5: (**a**) Congestion rate prediction in each time slot, (**b**) latency, (**c**) FL model drop ratios, and (**d**) FL model delivery ratios between the proposed and reference schemes. aggregation scheduling failures. In contrast to the drop ratio, the FL model delivery ratio is presented in Figure 5d. This parameter measures the successful model updates. A high model delivery ratio is significant for ensuring that the latest model updates from edge devices are successfully integrated into the central model. The proposed approach reached a delivery ratio of 99.9922%, which is applicable for IIoT application services following the criteria standardization. The proposed approach has delivery ratios 2.2541% and 3.5081% higher than MLSTM-CCFL and CLSTM-CFL, respectively. By identifying future congestion intervals, a proactive model aggregation update orchestrates the forwarding path to optimize matching local-aggregator throughputs and minimize total latency, as described in Equation (4).