## 5 Results
### Client Performance Analysis  
The deployment of FedSAGE requires each client to locally perform inference using a pre-trained VAE in order to extract mean latent vectors \(\overline{\mu_{i}}\). This introduces additional computational overhead compared to conventional FL frameworks, as clients must execute an extra forward pass through the VAE. To assess the practicality of this design, especially in edge environments with limited resources, we evaluate the lightweight nature of the VAE component in terms of both runtime and memory consumption. Specifically, we compare the VAE's inference time and peak RAM usage against the baseline DNN models used for DGA classification (CNN, BiLSTM, and Transformer), across clients with 2, 4, 6, and 8 virtual CPU cores. This analysis demonstrates that the integration of the VAE imposes minimal burden on client devices, ensuring that FedSAGE remains compatible with typical federated learning infrastructures.  
As shown in Figure 7, clients with fewer cores experience significantly higher execution times. For example, training the Transformer model takes over 23 s on a 2-core VM, while it only takes 11.48 s on an 8-core VM. BiLSTM and CNN exhibit similar trends. In contrast, inference with the VAE remains lightweight across all configurations, with average execution time below 1 s, even on the lowest-resource devices. This confirms that latent extraction can be executed in near real time without affecting system responsiveness. Furthermore, since clustering and selection are performed only once per training round, not per sample, the system supports batch-based operation and asynchronous training, making FedSAGE practical for real-world, low-latency federated inference pipelines, especially for edge and IoT devices.  
These results validate the effectiveness of FedSAGE's client selection strategy outlined in Section 3.3. By selecting the top \(\rho\%\) fastest clients in each data cluster, the framework effectively avoids performance degradation due to stragglers. This approach ensures both training efficiency and fair representation of heterogeneous data sources.  
In addition to execution time, we evaluated the memory consumption of each model during local training and inference. Table 6 reports the average RAM usage (in GB) measured on client devices for the three deep learning models and the VAE.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model Name** & CNN & BiLSTM & Transformer & VAE \\ \hline
**RAM Usages (GB)** & 0.62 & 0.63 & 0.68 & 0.10 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Average memory usages (in GB) for different DNN models.  
The results show that all of the models maintained relatively low memory footprints, with Transformer being the most memory-intensive at 0.68 GB, followed closely by BiLSTM and CNN. The VAE, used for latent vector extraction and clustering, had the smallest memory demand at only 0.10 GB, making it well-suited for deployment on edge devices with _limited resources_.  
These findings further support the practical applicability of FedSAGE in resource-constrained environments, where both compute time and memory usage must be considered in client selection and system design. This is well suited for the system architecture built with VAE, as shown in Section 3.2.