## 4 Experiment Results and Analysis  
### Experimental Framework and Dataset  
In this study, the FLOWER federated learning framework was used as the implementation framework for the proposed federated learning algorithm, as shown in Figure 3. The FLOWER framework, developed by the German company Adap, has high scalability and supports several machine-learning frameworks, including Pytorch, TensorFlow/Keras, and scikit-learn [29]. The framework is designed based on practical scenarios, enabling it to manage a large number of client loads and operate with heterogeneous client operating systems. Consequently, numerous researchers have used the FLOWER framework extensively as a federated learning implementation framework [30; 31].  
Figure 3: Federated learning with FLOWER framework.  
Figure 2: Learning results for different initial \(\beta\) values (\(\beta=0.3\), \(0.5\), \(0.7\)).  
In this study, the image recognition task is chosen as the training target for the federated learning neural network model, and three image datasets including Fashion-MNIST [32], CIFAR-10 [28], and CIFAR-100 [28] are used to train the neural network model. These three datasets are commonly used for training neural network models for deep learning. The Fashion-MNIST dataset mainly contains popular fashion items, including T-shirts/tops, pants, pullovers, dresses, coats, sandals, shirts, sneakers, bags, and boots, where images are grayscale and have a size of 28 pixels. The CIFAR-10 dataset contains 10 image categories including airplanes, cars, birds, cats, deer, dogs, frogs, horses, ships, and trucks, and each image has a size of 32 pixels. However, the primary difference between the CIFAR-10 and Fashion-MNIST datasets is that each image in the CIFAR-10 dataset has three color channels, which makes the training of the neural network model more difficult. The CIFAR-10 and CIFAR-100 datasets were collected and proposed by the same researchers, and both datasets have the same image size and number of channels. However, the CIFAR-100 dataset has up to 100 image categories, including 20 classifications (superclasses) such as household appliances, furniture, and insects. Each classification can be further divided into five subcategories, for example, the household appliance classification consisting of clocks, keyboards, lamps, phones, and TVs. Although the image shapes are the same as those of the CIFAR-10 dataset, the significantly increased number of image categories makes the training of the neural network model on the CIFAR-100 dataset much more complex than that on the CIFAR-10 dataset. Thus, an increase in the number of neural network layers is required to achieve sufficient prediction performance on the CIFAR-100 dataset. In this study, the Dirichlet function is used to partition the dataset to meet various heterogeneous data situations of clients, and the variable \(\alpha\) is used to control the non-IID degree in the Dirichlet function. When \(\alpha\to 0\), this indicates that the generated dataset has a severe non-IID degree, and when \(\alpha\to 1\), this indicates that the generated dataset tends to have an ideal distribution of IID. The federated learning-related parameters are listed in Table 2.