## 5 Experiments and Results  
Two experiments are conducted. The first experiment is conducted on SVHN [22] and CIFAR-10 [23] datasets for an image classification task in a simulated federated learning environment. The environment is a cluster consisted of virtual nodes. A single central node that acts as the central server is connected to all other nodes, which are the local clients. Each client has a connection only to the server. Accessing another client is prohibited. The federated learning environment consists of nine clients. The training dataset is split in two to form a non-IID settings for biased client cluster. Clients within the cluster share the same classes but no class is shared among clusters. The first setting has two minority clients and seven majority clients, and the second setting has two, three, and four clients for each three clusters. For simplicity, we assumed every client is utilized in every communication round and contributes to updating the global model. The same shallower modification of ResNet in Section 3.3 is used as a backbone model \(f\) with an Adam optimizer. Hyperparameters are learning rate \(\eta\) 0.001, weight decay 0.0, batch size 64, local epochs \(E\) 10, communication rounds 200, and event period \(t\) 1. The test results are presented in Tables 4 and 5. According to the results, in both datasets, SVHN (Table 4) and CIFAR-10 (Table 5), FedWAvg achieves higher accuracy than previous methods for both environment. Still, FedWAvg performance is sensitive to the _alpha_ hyperparameter.  
The second experiment compares the change of the number of forgettable examples over rounds. The experiment setup is nearly the same but with the following changes. There are only two clusters containing two clients and eight clients each. A cluster has two classes designated. The dataset used is CIFAR-10. Each client contains 1200 non-overlapping CIFAR-10 samples. Batch size is increased to 128 and communication round is 500. \(\alpha\) is 0.3. Tables 6-8 present the number of forgettable examples by three federated learning methods. FedAvg (Table 6) and FedProx (Table 7) show nearly no improvement of reducing forgettable examples. On the other hand, FedWAvg (Table 8) have the number of forgetting examples in half by 200 rounds, although it seems to fluctuate after.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **2:7 Top** & **2:7 Average** & **2:3:4 Top** & **2:3:4 Average** \\ \hline FedAvg [1] & 53.83 & 53.49 & 48.29 & 34.62 \\ FedProx [11] & 70.60 & 57.89 & 73.83 & 59.14 \\ SCAFFOLD [12] & 53.09 & 50.28 & 19.01 & 15.02 \\ FedNova [13] & 56.33 & 52.25 & 42.11 & 32.21 \\ \hline FedWAvg (\(\alpha=0.1\)) & 57.27 & 50.31 & 72.91 & 37.16 \\ FedWAvg (\(\alpha=0.2\)) & 76.07 & 54.60 & 87.37 & 63.84 \\ FedWAvg (\(\alpha=0.3\)) & 92.37 & 65.55 & 88.47 & 67.00 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Test accuracy (%) for SVHN.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **2:7 Top** & **2:7 Average** & **2:3:4 Top** & **2:3:4 Average** \\ \hline FedAvg & 61.28 & 45.94 & 45.20 & 36.85 \\ FedProx & 48.35 & 46.32 & 46.55 & 39.52 \\ SCAFFOLD & 44.35 & 42.13 & 31.03 & 27.18 \\ FedNova & 54.53 & 50.79 & 48.03 & 39.44 \\ \hline FedWAvg (\(\alpha=0.1\)) & 68.55 & 49.38 & 70.85 & 41.56 \\ FedWAvg (\(\alpha=0.2\)) & 51.80 & 46.26 & 74.70 & 50.92 \\ FedWAvg (\(\alpha=0.3\)) & 80.83 & 50.07 & 75.27 & 51.69 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Test accuracy (%) for CIFAR-10.