## 4 Results and Discussion
### Pruning Performance Comparison  
To evaluate the performance of the DualFEL, we conducted comparative experiments with several baseline algorithms, including global FL (FedAvg [1], FedProx [29], and FedLP [48]), and personalized FL (pFedMe [30], FedKD [49], FedALA [50], and Per-FedAvg [27]). We initially conducted federated training on the Fashion-MNIST and CIFAR-10 datasets following the Dirichlet (\(\alpha=0.5\)) distribution and the IID distribution to illustrate the accuracy comparisons among various algorithms. Table 2 below first presents the accuracy comparison results of different algorithms. It was observed that the performance of the personalized FL baselines shows a notable decline under IID distribution, while DualFEL exhibits a smaller decrease in performance under both IID and non-IID data. Compared to the global FL algorithms, DualPFL achieves the highest accuracy and a significant improvement. This can be attributed to the sparse mask generation method to tackle the statistical heterogeneity, which mitigates the redundancy of parameters in local gradient updates. We then evaluated the performance with \(\alpha\) values of 0.5, 0.7, 1, and 10. We observed that the accuracy of the DualPFL approach is higher than that of the FedAvg [1] and FedLP [7] schemes, as shown in Figure 3. When the label distribution is highly imbalanced (\(\alpha=0.5\)), the accuracy curve of DualPFL is smoother compared to the other two methods, without significant fluctuations. This indicates that our algorithm maintains a stable performance throughout the training process. The improvements in accuracy are attributed \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Schemes**} & \multicolumn{2}{c}{**Fashion-MNIST**} & \multicolumn{2}{c}{**CIFAR-10**} \\ \cline{2-5}  & **IID** & **Non-IID (\(\alpha=0.5\))** & **IID** & **Non-IID (\(\alpha=0.5\))** \\ \hline FedAvg [1] & 89.2 & 86.37 & 70.77 & 58.23 \\ FedProx [29] & 89.3 & 86.41 & 72.35 & 59.12 \\ FedLP [48] & 85.49 & 82.69 & 69.82 & 56.25 \\ \hline pFedMe [30] & 89.57 & 96.13 & 66.09 & 89.91 \\ FedKD [49] & 83.76 & 95.02 & 52.73 & 88.72 \\ \hline FedALA [50] & 89.21 & 96.29 & 73.42 & 90.23 \\ Per-FedAvg [27] & 87.67 & 92.12 & 63.17 & 67.72 \\ \hline DualPFL & 94.68 & 97.76 & 86.87 & 91.64 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison results of several FL algorithms under IID and Non-IID data. to the personalized aggregation weighting coefficients set for each client in cases of label skew, ensuring that the global model does not deviate, thereby enhancing test accuracy. Table 3 presents a comparative analysis of the accuracy and communication overhead on the COCO dataset. The experimental results show that DualPFL consistently outperforms both FedAvg [1] and FedLP [7] across multiple performance metrics. Specifically, DualPFL achieves a maximum compression ratio of 67% while maintaining model utility. Moreover, it exhibits remarkable efficiency by reaching 80% mAP more than 2\(\times\) as fast as competing methods. This is attributed to the representation augmentation mechanism and the utilization of inverse KL divergence as a weight coefficient to maintain model utility. These results underscore the effectiveness of our method in balancing model compression, accuracy, and training efficiency. DualPFL demonstrates significant communication efficiency by reducing both per-round communication time and total data transmission volume, as evidenced in Table 4. Through initial sparse pre-training and the subsequent dynamic evaluation of gradient change rates to adjust channel sparsity ratios, DualPFL addresses inherent issues of label skew and device heterogeneity. Our experimental results confirm that this approach successfully balances the trade-off between model performance and communication efficiency in heterogeneous federated learning.