## 5 Results
### Distributed Training Performance  
The experimental results shown in Tables 4 and 5 present a comparison of Top-1 test accuracy achieved by different FL algorithms under different training durations. This demonstrates that FedSAGE consistently outperforms all baseline methods in terms of both accuracy and convergence speed across different deep learning models and data distribution settings.  
Under uniform data with the CNN model, FedSAGE reaches the highest accuracy of 87.53% within 800 s, significantly outperforming FedAvg (76.69%) and FedSpeed (65.26%). In the non-uniform scenario, FedSAGE again achieves the best result, with 87.85%, showing superior robustness to non-IID data compared to other methods such as FedAvg (77.85%) and FedRand (78.48%).  
With the BiLSTM model, FedSAGE attains its peak accuracy of 89.33% in just 500 s for uniform data, while other methods such as FedAvg and FedSpeed achieve lower accuracy  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**DNN Model**} & \multirow{2}{*}{
\begin{tabular}{c} **Training** \\ **Time (s)** \\ \end{tabular} } & \multicolumn{6}{c}{**Top-1 Accuracy (\%)**} \\ \cline{3-7}  & & **FedAvg** & **FedSAGE** & **FedRand** & **FedSpeed** & **FLIS** \\ \hline \multirow{4}{*}{CNN} & 400 & 74.08 & 78.36 & 72.20 & 62.63 & 49.66 \\ \cline{2-7}  & 600 & 74.08 & 84.65 & 72.66 & 63.58 & 72.14 \\ \cline{2-7}  & 800 & 76.69 & 87.53 & 77.19 & 65.26 & 72.82 \\ \hline \multirow{4}{*}{BiLSTM} & 200 & 79.64 & 80.79 & 77.57 & 75.79 & 60.52 \\ \cline{2-7}  & 350 & 80.71 & 86.96 & 80.13 & 81.81 & 70.61 \\ \cline{2-7}  & 500 & 82.10 & 89.33 & 81.93 & 84.16 & 73.85 \\ \hline \multirow{4}{*}{Transformer} & 500 & 63.02 & 65.98 & 61.52 & 64.30 & 53.37 \\ \cline{2-7}  & 1000 & 63.70 & 64.37 & 70.55 & 69.71 & 54.42 \\ \cline{1-1} \cline{2-7}  & 1500 & 64.86 & 76.09 & 74.80 & 65.64 & 55.76 \\ \hline \end{tabular}
\end{table}
Table 4: Top-1 test accuracy (%) for uniformly distributed client data.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**DNN Model**} & \multirow{2}{*}{
\begin{tabular}{c} **Training** \\ **Time (s)** \\ \end{tabular} } & \multicolumn{6}{c}{**Top-1 Accuracy (\%)**} \\ \cline{3-7}  & & **FedAvg** & **FedSAGE** & **FedRand** & **FedSpeed** & **FLIS** \\ \hline \multirow{4}{*}{CNN} & 400 & 70.60 & 82.47 & 70.52 & 78.78 & 50.34 \\ \cline{2-7}  & 600 & 75.28 & 84.11 & 70.52 & 81.38 & 59.22 \\ \cline{2-7}  & 800 & 77.85 & 87.85 & 78.48 & 85.66 & 66.24 \\ \hline \multirow{4}{*}{BiLSTM} & 200 & 78.29 & 81.86 & 75.10 & 78.96 & 49.66 \\ \cline{2-7}  & 350 & 79.14 & 87.62 & 77.89 & 82.48 & 49.66 \\ \cline{1-1} \cline{2-7}  & 500 & 80.27 & 89.83 & 80.26 & 85.38 & 49.66 \\ \hline \multirow{4}{*}{Transformer} & 500 & 65.12 & 66.96 & 62.64 &within the same timeframe. In the non-uniform case, FedSAGE further improves to 89.88%, demonstrating its ability to quickly converge while maintaining high performance even with heterogeneous data.  
For the Transformer model, which generally requires longer training times, FedSAGE still achieves the highest accuracy in both settings. It reaches 76.09% under uniform and 80.32% under non-uniform data after 1500 s, outperforming all other approaches, including FedSpeed and FLIS.  
Overall, these results confirm that FedSAGE not only accelerates the training process but also achieves superior accuracy, especially in realistic non-IID scenarios. Its ability to effectively cluster clients and select computationally suitable participants allows it to strike an optimal balance between efficiency and performance.