## 6 Experimental Evaluation
### Results and Discussion  
In this study, we presented four different scenarios that utilized Federated Workload Profiling (FWP) to optimize the resource utilization of edge computing nodes. In Scenario 1, we evaluated the memory usage of five different machine learning models before and after applying FWP optimization techniques. On an average, applying FWP led to a 47.8% reduction in memory usage across all machine learning algorithms. The algorithms that benefited the most from FWP in terms of memory usage reduction were Random Forest Tree and Decision Tree, with reductions of 73.9% and 69.3%, respectively. XGBoost and LightGBM had the smallest reductions in memory usage, at 53% and 43.5%, respectively. We found that Random Forest Tree was the best-performing model in terms of memory consumption and used it for the rest of the experiments. In Scenario 2, we analyzed the impact of FWP on disk space usage and network I/O statistics. Our proposed optimization strategies resulted in slightly lower disk space usage and decreased network I/O after implementing FWP. Specifically, the total disk space used decreased by 336,304 bytes. With the network I/O statistics analysis, we can see that the on an average the number of bytes sent by a node decreased by 2,623,071 bytes (or about 1.4%), while the number of bytes received by a node decreased by 40,266 bytes (or about 0.04%). In Scenario 3, the cumulative impact of FWP across expanding node networks was examined. As the number of nodes in the federation increased, FWP demonstrated its effectiveness in optimizing memory resources, resulting in a notable reduction in memory  
Figure 13: FWP enabled FPSN performance. utilization of approximately 9%. Additionally, FWP showcased its ability to efficiently manage network traffic, leading to significant network traffic savings of around 27%. These findings underscore the scalability and positive impact of FWP in enhancing system performance and resource allocation as federated network nodes increase. In Scenario 4, we focused on evaluating the impact of FWP on the convergence time and memory utilization of machine learning algorithms. Our results showed that the execution time of the evaluated algorithms was significantly reduced, with Decision Tree and Random Forest performing the best in terms of faster convergence. The execution time decreased after FWP was applied, with percentage decreases ranging from 19.2% to 62.3%. Specifically, the execution times for Random Forest, Decision Tree, GBDT, XGBoost, and LightGBM decreased by 19.2%, 33.3%, 53.9%, 62.3%, and 57.9%, respectively. For the Random Forest tree depth and training time before and after FWP, we found that the average tree depth decreased by 3.34, or about 27.5%, after FWP was applied which will in turn lead to a reduction in the amount of memory. Similarly, the average execution time decreased by 24.68 ms, or about 20.4%, after FWP was applied. These results suggest that FWP had a significant positive impact on both tree depth and training time for Random Forest across all nodes. Finally, in Scenario 5, we utilized FWP to aid in edge node selection and FPSN accuracy calculation. We removed an underperforming edge node and only selected trustworthy nodes to use in our FPSN calculation. On an average, applying FWP led to a 6.8% increase in accuracy across all nodes. However, it is worth noting that the degree of improvement varied across different nodes, with Node 1 having the largest increase in accuracy (8%), and Node 4 having the smallest increase (4%). Overall, the results of our experiments demonstrate the effectiveness of FWP in optimizing the resource utilization of edge computing nodes. The implementation of our proposed optimization strategies yielded substantial reductions in memory usage and modest improvements in disk space efficiency. Concurrently, these strategies enhanced network I/O performance and led to more efficient convergence times for machine learning algorithms.