## 5 Results and Discussion
### Performance Analysis of FedLO
PSO displays steady improvement, reaching an accuracy of 0.8746, demonstrating compelling exploration and exploitation. SCA exhibits a consistent positive trend, achieving an accuracy of 0.8867, indicating its adaptability to the federated data from the rice leaf disease dataset. GWO demonstrates gradual improvement, reaching an accuracy of 0.8908, showcasing a compelling exploration of the federated data space. The proposed LO method shows competitive performance, achieving an accuracy of 0.9027 and outperforming some established algorithms in convergence and accuracy. Figure 6(b) shows the performance of various algorithms in FL using the WaRP dataset over 30 rounds. C = 0.1 exhibits consistent improvement, reaching a peak accuracy of 0.7829. Similarly, C = 0.2 demonstrates positive trends, achieving a maximum accuracy of 0.7948. C = 0.5 showcases substantial improvement, reaching an accuracy of 0.8139, while C =  
of 0.8709, surpassing some established optimization algorithms in terms of convergence and accuracy with the WaRP dataset. Figure 8 shows the communication cost of different algorithmic approaches to FL on distributed datasets. It plots the communication cost for different values of C and several federated learning algorithms: FedPSO, FedSCA, FedGWO, and FedLO. According to the figure, the proposed algorithm has the lowest communication cost of 0.50 across all values of C. This result suggests that the proposed algorithm draws inspiration from cooperative foraging behavior in lemurs and is more efficient in communication than other algorithms based on different metaheuristic techniques. In particular, the FedLO algorithm can lower the communication overhead by up to 15% compared to standard federated learning approaches. Model Training Dynamics and Efficacy: A Deep Dive into Rice Leaf Disease and WaRP Datasets with FedLO Methodology  
In the context of the rice leaf disease dataset, the presented results underscore the effectiveness of the FedLO method in enhancing model performance (see Figures 9 and 10). Across 30 training epochs, training and validation accuracy exhibited noticeable improvement. The training accuracy initiated at 21.48% in the first epoch and demonstrated a consistent upward trend, culminating at 87.50% in Epoch 30. Concurrently, the validation accuracy progressed from 28.12% to 82.81% over the same period. Complementing the accuracy metrics, the loss function, a crucial indicator of model efficacy, consistently decreased during training. The training loss started at 1.54 and steadily diminished to 0.29 by Epoch = 30. Similarly, the validation loss showed a reduction from 1.37 to 0.46. Occasional marginal increments in validation loss, notably in Epoch 2 and Epoch 8, suggested potential episodes of overfitting (see Figure 10). The general observations highlighted initial challenges in achieving satisfactory accuracy, which were gradually overcome as the model adapted and refined its predictions. The model attained a peak training accuracy of 87.50% and a validation accuracy of approximately 82.81% by Epoch 30. The diminishing loss function underscored the model's ability to extract meaningful insights from this dataset, enhancing its predictive capabilities. In conclusion, these results, obtained through the application of FedLO in this dataset, affirm the methodology's efficacy in progressively refining model performance over multiple training epochs. Further insights could be gained by delving into additional details on this dataset's characteristics, the employed model architecture, and specific nuances in implementing FedLO. In the investigation of the WaRP dataset, the FedLO was applied to assess and enhance model performance (see Figures 11 and 12). The training spanned 30 epochs, and both training and validation accuracies and the loss metrics were tracked. Figure 8: Comparing communication costs of different algorithms. Figure 11: Training and validation accuracy—WaRP dataset. Figure 10: Training and validation loss—rice leaf disease dataset.