# pFedBASC: Personalized Federated Learning with Blockchain-Assisted Semi-Centralized Framework
## 5 Experiments
### Performance Evaluation of pFedBASC  
Table 2 displays the personalized accuracy for independent and identically distributed (iid) data and non-iid data under the same communication round limit. The performance advantage of pFedBASC is not obvious when starting with the simpler MNIST. This obser \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**System**} & \multicolumn{2}{c}{**Non-System**} & \multirow{2}{*}{
\begin{tabular}{c} **Training** \\ **Time Incre** \\ **Ease Ratio** \\ \end{tabular} } \\ \cline{2-2}
**Method** & \begin{tabular}{c} **Accuracy** \\ **Runtime** \\ \end{tabular} & \begin{tabular}{c} **Device** \\ **Runtime** \\ \end{tabular} & \begin{tabular}{c} **Accuracy** \\ **Runtime** \\ \end{tabular} & \
\begin{tabular}{c} **Accuracy** \\ **Runtime** \\ \end{tabular} &
\begin{tabular}{c} **Device** \\ **Ease Ratio** \\ \end{tabular} \\ \hline FedAvg & 60.96\% & 36.67\% & 63.44\% & 45.82\% & 2.06\(\times\) \\ \hline FedProx & 58.98\% & 34.45\% & 63.26\% & 44.75\% & 2.31\(\times\) \\ \hline FedDyn & 59.74\% & 35.29\% & 61.33\% & 47.68\% & 2.38\(\times\) \\ \hline FedGen & 57.76\% & 36.45\% & 60.85\% & 49.73\% & 2.07\(\times\) \\ \hline FedBN & 60.24\% & 32.29\% & 62.91\% & 42.2\% & 2.04\(\times\) \\ \hline FedBabu & 64.01\% & 36.06\% & 66.79\% & 42.59\% & 2.02\(\times\) \\ \hline FedPer & 63.54\% & 36.66\% & 65.76\% & 43.9\% & 1.93\(\times\) \\ \hline FedRep & 65.6\% & 32.66\% & 68.19\% & 42.64\% & 2.72\(\times\) \\ \hline pFedLa & 57.71\% & 36.91\% & 60.26\% & 45.92\% & 2.25\(\times\) \\ \hline pFedBASC & **65.73\%** & **100\%** & **69.8\%** & **100\%** & **1.38\(\times\)** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The experimental results of pFedBASC performance under system heterogeneity and non-system heterogeneity situations on CIFAR-10. vation is largely due to the straightforward nature of the MNIST dataset and the relatively simple model structure employed. In such scenarios, even the basic FedAvg suffices to capture most dataset features. Continuing to use HN would introduce unnecessary computational overhead and complicate the training process, potentially deteriorating outcomes. Despite this, it is noteworthy that there is only a small gap between our method and the optimal value method in MNIST. This is because each method can readily accomplish the training task with MNIST, which necessitates further experiments on the remaining datasets. It is also worth noting that HNs were initially designed for more complex datasets and model architectures. The strength of the result on the FMNIST dataset is not consistently apparent, and although pFedBASC demonstrates some advantages in iid data, the difference compared to other methods remains marginal. The advantage of pFedBASC becomes more apparent when utilized in more complex models and datasets (CIFAR series). This is because, in complex tasks, model parameter selection may be more critical, and HN's embedding vector can provide more suitable parameters by adaptively generating higher-accuracy models. As demonstrated in Table 3, pFedBASC ensures the highest accuracy while maintaining reasonable communication costs, suggesting an optimal balance between model accuracy and communication efficiency. Subsequent experiments revealed that although FedFomo exhibits minimal communication overhead on the CIFAR-10/100 datasets, its accuracy significantly lags behind that of our pFedBASC.