# FedRDS: Federated Learning on Non-IID Data via Regularization and Data Sharing
## 4 Experiments and Analysis
### Ablation Experiment  
Figure 6 demonstrated that using either the regularization term alone or data sharing was more effective than FedAvg, and both approaches mitigated the non-IID problem while improving model accuracy. Combining dynamic regularization and data sharing can give the FL algorithm the best of both worlds. Also, the results confirm our speculation that both dynamic regularization and data-sharing strategies can mutually promote each other.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**FL Method**} & \multicolumn{4}{c}{**Dataset**} \\ \cline{2-9}  & \multicolumn{2}{c}{**MNIST**} & \multicolumn{2}{c}{**Fashion-MNIST**} & \multicolumn{2}{c}{**SVHN**} & \multicolumn{2}{c}{**CIFAR-10**} \\ \cline{2-9}  & _Rounds_ & _Speedup_ & _Rounds_ & _Speedup_ & _Rounds_ & _Speedup_ & _Rounds_ & _Speedup_ \\ \hline FedAvg & 100 & 1\(\times\) & 100 & 1\(\times\) & 100 & 1\(\times\) & 100 & 1\(\times\) \\ FedProx & 74 & 1.35\(\times\) & 95 & 1.05\(\times\) & 79 & 1.26\(\times\) & 92 & 1.09\(\times\) \\ MOON & 61 & 1.64\(\times\) & 83 & 1.2\(\times\) & 42 & 2.38\(\times\) & 64 & 1.56\(\times\) \\ FedRDS & 40 & 2.5\(\times\) & 67 & 1.49\(\times\) & 36 & 2.78\(\times\) & 57 & 1.75\(\times\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Communication efficiency comparison under the default setting.  
Figure 6: Ablation Experiment.  
We further investigate the specific performance of Fed regularization and Fed data sharing. The results show that the accuracy of Fed regularization in Figure 6b is low in the early training period due to the high computational complexity of introducing regularization terms and the more complex data classes of CIFAR-10 compared to MNIST. But, as the model continues to learn, Fed regularization eventually outperforms the benchmark model FedAvg. Fed data sharing is achieved by sharing server-side data rather than client-side data. Fed data sharing performs better than Fed regularization, which indicates that narrowing the data distribution gap significantly impacts the experimental results more than regular terms. Nevertheless, if a is set too large, it will make the distribution of client-side data too similar to that of server-side data, which elevates the possibility of compromising privacy. The experimental results show that using the regularization term alone or data sharing can significantly alleviate the non-IID problem and boost the model's robustness. However, when updating the weights, the combined approach of combining regularization terms and data sharing makes the local loss function more complex, and FedRDS takes longer to converge.  
As a regularization and data sharing strategy for FL, FedRDS offers a unique solution to some challenges that arise when dealing with non-IID data. By combining a regularization term with the data-sharing strategy, the algorithm effectively mitigates the issue of uneven model distribution and overfitting, resulting in better model generalization. The results of the experiments indicate that FedRDS outperforms the benchmark models. Our findings suggest that dynamic regularization and the data-sharing strategy have significant practical applications for FL-related problems, including reducing the effects of overfitting and heterogeneous model distributions as well as improving generalization and accuracy in non-IID data settings. Therefore, dynamic regularization and data sharing is of great significance and value and has broad application prospects for solving problems in FL networks, which will help improve the efficiency, accuracy, and robustness of various FL applications in such diverse fields as healthcare, finance, and manufacturing.