## 8 Conclusions and Future Work  
This paper presented RBPS, a centralized strategy with cooperative intent and an adaptive client selection strategy designed to address the challenges of heterogeneity, battery constraints, and privacy sensitivity in real-world learning environments. By integrating three critical factors (accuracy contribution, battery cost, and privacy sensitivity) into a dynamic reward-based scoring mechanism, RBPS enables the selection of suitable clients without overloading any specific device category. The strategy was evaluated using simulated pools of smartphones, edge devices, and IoT sensors across four benchmark datasets, under diverse client pool sizes and device distributions. Comparative experiments demonstrated that RBPS consistently achieves a more balanced trade-off between model performance, battery efficiency, and privacy preservation when compared to base \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Method** & **Accuracy** & **Battery Efficiency** & **Privacy** & **Remarks** \\ \hline \hline \multicolumn{1}{c}{Oort} & \multicolumn{1}{c}{Very High} & \multicolumn{1}{c}{Poor} & \multicolumn{1}{c}{Poor} & \multicolumn{1}{c}{Fastest convergence; inefficient and privacy-leaking.} \\ \hline \multicolumn{1}{c}{FedCS} & \multicolumn{1}{c}{Medium} & \multicolumn{1}{c}{Moderate} & \multicolumn{1}{c}{Moderate} & \multicolumn{1}{c}{Resource-matching; lacks privacy-awareness.} \\ \hline \multicolumn{1}{c}{FRS} & \multicolumn{1}{c}{Medium} & \multicolumn{1}{c}{High} & \multicolumn{1}{c}{Moderate} & \multicolumn{1}{c}{Fair and battery-efficient; not privacy-aware.} \\ \hline \multicolumn{1}{c}{FedProm} & \multicolumn{1}{c}{Low} & \multicolumn{1}{c}{High} & \multicolumn{1}{c}{Best} & \multicolumn{1}{c}{Maximizes privacy and personalization; lower accuracy.} \\ \hline \multicolumn{1}{c}{
\begin{tabular}{p{56.9 pt}} \\ (Ours) \\ \end{tabular} } & \multicolumn{1}{c}{High} & \multicolumn{1}{c}{Moderate} & \multicolumn{1}{c}{Strong} & \multicolumn{1}{c}{Balanced across all objectives; dynamic and cooperative.} \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of FL client selection strategies vs. RBPS. Figure 35: Battery level over 200 rounds on MNIST. Figure 36: Privacy score over 200 rounds on MNIST. line and SOA approaches. RBPS achieved an accuracy improvement of up to 25% over SOA methods such as Oort and FedCS on the MNIST dataset. When compared to single-metric baselines across all datasets, RBPS provided better balance among accuracy, energy consumption, and privacy impact. In heterogeneous client pools, the method preserved model stability while reducing battery usage variance and limiting privacy exposure. As the number of clients increased, RBPS maintained convergence with minimal degradation in accuracy, demonstrating robustness and scalability in complex FL scenarios. While this study is based on detailed simulation modeling, all operational parameters including battery consumption patterns, device heterogeneity, and privacy risk factors were derived from realistic hardware characteristics and empirically informed system behavior. While the current study relies on simulation-based evaluation, which is standard in early-stage FL research, we acknowledge that further validation on physical hardware is an important next step. Implementing RBPS on real embedded and mobile devices will allow us to observe potential runtime effects related to hardware-level variability, resource usage, and network dynamics. This transition from simulation to deployment is part of our roadmap and will strengthen the practical applicability of our approach. In future work, we aim to deploy RBPS on a heterogeneous testbed composed of embedded and mobile devices to empirically measure key performance indicators such as per-round battery consumption, communication latency, and memory utilization.