## 5 Results and Discussion
### Comprehensive Model Architecture and Constant Configuration  
This section presents a detailed analysis of the proposed model for dataset classification, illustrated in Figure 4. This architecture comprises a total of fourteen layers, including four convolutional layers (conv2d, conv2d_1, conv2d_2, conv2d_3) responsible for extracting hierarchical features. Additionally, four max pooling layers (max_pooling2d, max_pooling2d_1, max_pooling2d_2, max_pooling2d_3) contribute to spatial dimension reduction. Furthermore, two flatten layers (flatten, dropout) transform the output into a one-dimensional vector, while two dense layers (dense, dropout_1) perform the final classification based on the learned features. The convolutional layers exhibit a progressive filter  
Figure 4: Detailed summary of the model architecture for dataset classification. Figure 3: Samples of WaRP dataset. increase, enabling the model to capture increasingly complex patterns. Output shapes, such as (None, 222, 222, 16) for the initial convolutional layer, represent the size of the data after each layer. The model demonstrates a moderate number of parameters, indicating a balanced level of complexity. The abundance of trainable parameters enhances the model's adaptability during training. The proposed CNN model is tailored to effectively learn intricate features from images, balancing complexity and efficiency during training. Including dropout layers contributes to regularization, mitigating the risk of overfitting. A thorough model summary, encompassing activation functions and evaluation metrics, would be imperative to assess its performance comprehensively. In this paper, a set of constants has been meticulously defined to shape the behavior and performance of the proposed framework. These constants encapsulate pivotal aspects such as batch processing, training epochs, and unique considerations for the FedAvg and the optimization algorithms. Notably, the batch size, client-epoch parameter, total number of epochs, \"C\" parameter (specifically for FedAvg), and the number of clients collectively represent the foundational building blocks of the model's configuration. The batch size, a critical determinant of the number of training instances processed in each iteration, is consistently set at 10 for both the optimization algorithms and the FedAvg approach. This choice reflects a strategic balance between computational efficiency and model performance. The client-epoch parameter, governing the number of training epochs completed by each client in the FL setup, is maintained at 5 for both optimization methods. This standardization ensures a uniform learning experience across clients, contributing to the overall stability of the training process. The total number of epochs, a fundamental factor influencing the duration and depth of the model training, is universally established at 30 for both the optimization algorithms and the FedAvg approach. This parameter plays a crucial role in determining the overall convergence and performance of the model. The \"C\" parameter, a specific consideration for the FedAvg, assumes values of 0.1, 0.2, 0.5, and 1.0. This parameter controls the number of clients during the aggregation process in FL. Furthermore, the number of clients participating in the FL process is constant at 10. This stability ensures consistent collaboration and diversity in training, contributing to the model's robustness and generalization capability. Using Keras, the model architecture is visually represented in Figure 5. This diagram provides an intuitive overview of the connections and flow within the neural network, emphasizing layers' arrangement and respective operations. Figure 5: Visual representation of the proposed CNN model architecture using Keras. The FedLO framework utilizes fixed values for the HRR and LRR parameters, as specified in the original LO algorithm. Specifically, the HRR parameter is set to 0.5, and the LRR parameter is set to 0.1.