## 5 Results and Discussion
### Unstable Network Experimental Result  
Random data drops were introduced during communication rounds between the client and server to simulate an unstable network environment. The evaluation involved varying degrees of data drop (0%, 10%, 20%, and 50%) across different datasets (CIFAR-10, MNIST, rice leaf disease, and WaRP). To ensure the validity of the results, the outcomes were averaged over ten experiments. Table 2 displays the accuracy of FedAvg and alternative algorithms at different failure rates. FedAvg consistently declined accuracy as the failure rate increased across all datasets. For example, in CIFAR-10, accuracy ranged from 60.22% (0% failure) to 54.62% (50% failure). Similarly, in MNIST, accuracy varied from 97.29% (0% failure) to 92.31% (50% failure), indicating a significant impact of communication failures on model performance. In the rice leaf disease dataset, FedAvg showed accuracy ranging from 85.97% (0% failure) to 81.57% (50% failure), highlighting the algorithm's vulnerability to increased failure rates across diverse datasets. Additionally, in the WaRP dataset, the accuracy of FedAvg ranged from 82.29% (0% failure) to 77.48% (50% failure), further underscoring the consistent trend of declining accuracy under adverse network conditions. FedPSO consistently outperforms FedAvg across datasets and failure rates, indicating its potential for maintaining accuracy in the face of communication failures. For instance, in CIFAR-10, FedLO achieved an accuracy range of 69.36% (0% failure) to 64.11% (50% failure). Similarly, in MNIST, FedLO outperformed FedAvg with an accuracy range of 97.57% (0% failure) to 91.54% (50% failure), showcasing its resilience in unstable network environments. Furthermore, FedSCA and FedGWO also demonstrate competitive performance. In the WaRP dataset, FedGWO achieves an accuracy range of 84.02% (0% failure) to 79.00% (50% failure), showcasing its ability to cope with communication challenges. However, it is notable that FedLO consistently outperforms the other algorithms, emphasizing its superiority in maintaining accuracy under varying failure rates. In the WaRP dataset, FedLO achieves an accuracy range of 87.10% (0% failure) to 82.33% (50% failure), consistently surpassing FedAvg, FedPSO, FedSCA, and FedGWO. This highlights FedLO as a promising alternative for robust FL in unstable network conditions. These instances emphasize the importance of considering alternative FL algorithms, particularly FedLO, to mitigate the impact of communication failures. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Algorithm** &
\begin{tabular}{c} **Failure Rate** \\ **50\%** \\ \end{tabular} & **20\%** & **10\%** & **0\%** \\ \hline CIFAR-10 & FedAvg, C = 1.0 & 0.5462 & 0.5731 & 0.5911 & 0.6022 \\  & FedPSO & 0.6411 & 0.6674 & 0.6812 & 0.6936 \\  & FedSCA & 0.6637 & 0.6923 & 0.7052 & 0.7141 \\  & FedGWO & 0.6676 & 0.6907 & 0.7092 & 0.7207 \\  & FedLO & 0.6879 & 0.7150 & 0.7320 & 0.7403 \\ \hline MNIST & FedAvg, C = 1.0 & 0.9231 & 0.9517 & 0.9619 & 0.9729 \\  & FedPSO & 0.9154 & 0.9448 & 0.9629 & 0.9757 \\  & FedSCA & 0.9354 & 0.9564 & 0.9680 & 0.9778 \\  & FedGWO & 0.9378 & 0.9602 & 0.9715 & 0.9863 \\  & FedLO & 0.9422 & 0.9665 & 0.9814 & 0.9914 \\ \hline Rice leaf disease & FedAvg, C = 1.0 & 0.8157 & 0.8411 & 0.8521 & 0.8597 \\  & FedPSO & 0.8184 & 0.8416 & 0.8612 & 0.8746 \\  & FedSCA & 0.8454 & 0.8695 & 0.8796 & 0.8867 \\  & FedGWO & 0.8435 & 0.8729 & 0.8857 & 0.8908 \\  & FedLO & 0.8617 & 0.8825 & 0.8932 & 0.9027 \\ \hline WaRP & FedAvg, C = 1.0 & 0.7748 & 0.8025 & 0.8151 & 0.8229 \\  & FedPSO & 0.7851 & 0.8071 & 0.8207 & 0.8308 \\  & FedSCA & 0.7805 & 0.8060 & 0.8227 & 0.8354 \\  & FedGWO & 0.7900 & 0.8144 & 0.8320 & 0.8402 \\  & FedLO & 0.8233 & 0.8530 & 0.8655 & 0.8710 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Accuracy against communication failure probability.