## 5 Experimental Evaluation
### Computation Efficiency  
Figure 15 shows a comparative study of the energy consumption related to a range of FL methods, namely, Fed_Share, Fed_SGD, Fed_AVG, Fed_Prox, and UA-FedRL. These methods were tested on two distinct datasets, MNIST and CIFAR-10. The normalized energy consumption, presented on the y-axis, effectively illustrates the efficiency of each method in terms of energy utilization. Each method's impact is separately depicted for both MNIST and CIFAR-10 datasets using sky-blue and salmon-colored bars, respectively. The experiment was designed to achieve a target accuracy of 90% for the MNIST dataset and 45% for the CIFAR-10 dataset. The energy consumption for each device during a communication round was calculated using the following equation:  
\[E_{i}(t)=P_{i}. \tag{31}\]  
The total communication cost for all devices at round t was then calculated as follows:  
\[E_{T}(t)=\sum_{i=1}^{N}E_{i}(t). \tag{32}\]  
The cumulative communication cost across all rounds to achieve the target accuracy was defined as:  
\[E_{\text{cumulative}}=\sum_{t=1}^{T}E_{T}(t). \tag{33}\]  
Lastly, the normalized communication cost for MNIST and CIFAR-10, given the target accuracies of 90% and 45%, respectively, was calculated as:  
\[E_{\text{normalized, MNIST}}=\frac{E_{\text{cumulative}}-E_{min,MNIST}}{E_{ max,MNIST}-E_{min,MNIST}}, \tag{34}\]  
where \(E_{min,MNIST}\) is the minimum communication cost achieved to reach the target accuracy. \(E_{max,MNIST}\) is the maximum possible communication cost given a 90% accuracy target for MNIST.  
\[E_{\text{normalized, CIFAR-10}}=\frac{E_{\text{cumulative}}-E_{min,CIFAR -10}}{E_{max,CIFAR-10}-E_{min,CIFAR-10}}, \tag{35}\]  
where \(E_{min,CIFAR-10}\) is the minimum communication cost achieved to reach the target accuracy. \(C_{max,CIFAR-10}\) is the maximum possible communication cost given a 45% accuracy target for the CIFAR-10 dataset.  
Figure 14: Comparative analysis of normalized communication cost across different federated learning methods on MNIST and CIFAR-10 datasets.  
The UA-FedRL method emerged as the most energy-efficient model across both datasets, acquiring the least normalized energy consumption values of 0.25 and 0.19 for MNIST and CIFAR-10, respectively. On the contrary, Fed_SGD consumed the most energy for the CIFAR-10 dataset with a value of 0.78, while Fed_AVG showed the highest energy use on the MNIST dataset at 0.86. These results highlight the energy efficiency of different FL methods when deployed on different datasets.  
Table 10 presents a comprehensive comparison of different federated learning algorithms, including Fed_Share, Fed_SGD, Fed_AVG, Fed_Prox, and UA-FedRL, across three metrics: accuracy (with 95% confidence intervals), communication cost, and energy consumption on both the MNIST and CIFAR-10 datasets. UA-FedRL demonstrated the highest accuracy on both datasets (96.87% on MNIST and 62.73% on CIFAR-10), which reflects its effectiveness in handling non-IID data distributions. Additionally, UA-FedRL had the lowest communication cost (0.19 for MNIST and 0.24 for CIFAR-10) and energy consumption (0.25 for MNIST and 0.19 for CIFAR-10) which demonstrates its efficiency in resource-constrained environments. Other methods like Fed_AVG and Fed_Prox achieved competitive accuracy but with higher communication costs and energy consumption, which indicates a trade-off between model performance and resource utilization.