## 4 Experiment Setup
### Baseline  
To comprehensively evaluate the effectiveness of our proposed FedSAGE framework, we compare it against the following baseline FL strategies:  
* FedAvg [5]: The standard FL algorithm in which all clients participate in each training round. Clients perform local training using their private data and send the updated model parameters to a central server, which aggregates them using weighted averaging. FedAvg does not account for system heterogeneity or data distribution skewness.
* FedRand (Random Selection): A strategy where a fixed number of clients is randomly selected in each round. This method reduces the training time by avoiding straggler clients but may overlook important data held by randomly excluded clients, especially under non-IID conditions.
* FedSpeed (Only Selection): This method selects only clients with high computational performance (e.g., faster devices) in each training round. While this improves system efficiency, it may exclude slower devices that possess critical or unique data, potentially hurting model generalization in non-IID settings.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Component** & **Description** \\ \hline \multirow{2}{*}{Client Devices} & 30 Virtual Machines (VMs), Ubuntu 20.04, Intel Xeon \\  & Silver 4210 CPUs \\ \multirow{2}{*}{System Heterogeneity} & Random CPU cores per VM: 1 to 8 cores \\ Communication Layer & RabbitMQ over LAN (1 Gbps bandwidth) \\ \multirow{2}{*}{Training Settings} & SGD (lr = 1 \(\times\) 10\({}^{-5}\), momentum = 0.9), batch size = 32, \\  & up to 400 rounds \\ \multirow{2}{*}{FL Framework} & Custom Python (3.8.10) + PyTorch (2.2.2) \\ \multirow{2}{*}{Selection Ratio} & \(\rho\) = 30\% (top fastest clients per cluster in FedSAGE \\  & and variants) \\ \multirow{2}{*}{Data Partitioning} & - Uniform: 5 K samples/label/client \\  & - Non-uniform: 2.5 K-7.5 K samples/label/client \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of experimental setup.  
* FLIS (Federated Learning via Inference Similarity) [11]: A clustered FL approach that groups clients based on inference similarity (i.e., the similarity of model outputs on a shared auxiliary dataset). Although FLIS effectively addresses data heterogeneity, it introduces additional overhead by requiring an auxiliary dataset for server-side evaluation and similarity computation.  
For selection algorithms, devices are selected with selection ratio \(\rho=30(\%)\). These strategies serve as comparative baselines to highlight how FedSAGE balances both training efficiency and data relevance through intelligent client clustering and selection.  
While our evaluation focuses on FL methods, we acknowledge that the frameworks we consider are general-purpose FL systems. Existing studies that explore FL in conjunction with domain-specific applications, such as DGA detection or IDS [4; 6; 7; 21; 22], primarily provide broad surveys or preliminary insights into the applicability of FL in security contexts. However, these works do not present strong contributions in key technical aspects that are central to our investigation, namely convergence speed optimization, intelligent client selection, and energy-aware system design. They lack systematic experimentation or in-depth evaluation along these axes. For this reason, we do not consider these studies as meaningful baselines for our work. Including them would dilute the focus of our evaluation without offering a fair or technically relevant point of comparison.