## 4 Results and Discussion
### Hardware Implementation Performance
The updated global model is then redistributed to all connected edge devices, ensuring continuous improvement across diverse healthcare settings. The dissipated energy measurements validated the SBCs' energy efficiency throughout the experiment. Raspberry Pi had the lowest dissipated energy of 0.013 mJ, demonstrating that, despite its greater power consumption, its faster processing speed substantially reduces energy loss. In contrast, Jetson Nano in 5 W mode had the maximum dissipated energy of 0.039 mJ, demonstrating that reduced power consumption does not always imply superior efficiency. Interestingly, Jetson Nano's MAXN mode, with a dissipated energy of 0.027 mJ, showed a better balance of power efficiency and processing capabilities, suggesting that deliberately allocating more power can enhance overall energy efficiency. Given these findings, Raspberry Pi remains the best choice for applications that need high-speed inference, although Jetson Nano in 5 W mode may be more ideal for power-constrained situations, as shown in Figure 10. However, when contemplating the long-term \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Specification**} & \multicolumn{4}{c}{**Evaluation Platform**} \\ \cline{2-4}  & **Jetson Nano** & **Jetson Nano** & **Raspberry Pi** \\  & **(5 W Power)** & **(MAXN Power)** & **Raspberry Pi** \\ \hline Detection time (s) & 0.048 & 0.029 & 0.01 \\ Power Consumption (W/h) & 2.948 & 3.385 & 4.825 \\ RAM utilization (MB) & 1960 & 1763 & 1948 \\ Dissipated Energy (mJ) & 0.039 & 0.027 & 0.013 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Hardware performance evaluation results. scalability of edge AI systems, particularly those utilizing Federated Learning (FL), energy efficiency becomes an important consideration in feature research. Rather than investigating different hardware platforms separately, this work demonstrates how incorporating the TensorFlow Lite-based model into small, low-cost SBCs provides a feasible step forward in AI-assisted skin cancer diagnosis. The fast inference speed of Raspberry Pi and the combination of power efficiency and processing power of Jetson Nano show that AI-assisted diagnosis can be efficiently implemented on low-cost hardware. These results demonstrate that SBCs are not only capable of classification, but also provide a cost-effective and scalable option for mobile, power-efficient medical AI applications. Furthermore, such a system can enable remote monitoring of patients in rural or underserved areas, ensuring access to early diagnosis and treatment recommendations without the need for high-end medical infrastructure. However, this study has limitations, including the lack of diverse clinical images, which may affect the generalizability of the model. Computational constraints of edge devices also limit the complexity of the model. Further validation in real-world settings is required to address environmental variability. Network conditions, sensor inconsistencies and patient movement can affect applicability. Explainable AI (XAI) can improve model interpretability for medical practitioners. Expanding the dataset with diverse skin colors and lesion types is critical for robustness. Future work should optimize accuracy and efficiency for edge devices. Figure 10: Comparison of hardware performance metrics.