## 6 Discussion  
The HAR dataset exemplifies the heterogeneity commonly found in IoT environments, where client-level data often exhibit non-iid. Despite this natural variability, as analyzed in Section 5.1.2, our hierarchical framework demonstrated consistent performance, maintaining stable global accuracy across training rounds. This highlights the robustness of the localized edge-level aggregation and adaptive weighting mechanisms, which effectively mitigate the impact of distributional differences. By leveraging these mechanisms, the framework achieves efficient global updates without bringing significant overhead or requiring extensive preprocessing. These results suggest that our framework is well-suited for diverse IoT scenarios, where client data may inherently deviate from idealized distributions. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Device** & **Comm. Time** & **Training Time** & **Round Time** & **En/Decryption** \\  & **(s)** & **(s)** & **(s)** & **Time (s)** \\ \hline IoT 1 & 4.35 & 4.13 & 8.48 & 0.064 \\ IoT 2 & 4.33 & 5.95 & 10.28 & 0.067 \\ IoT 3 & 3.94 & 5.86 & 9.80 & 0.066 \\ IoT 4 & 4.11 & 5.70 & 9.81 & 0.064 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of communication, training, round time, and encryption impact on the federated-learning process. The experimental results unequivocally demonstrate that our asynchronous aggregation strategy, in conjunction with lightweight encryption, achieves comparable model accuracy to HierFAVG while providing significant benefits in communication efficiency and resilience to device heterogeneity. Specifically, the reduced round-time cost observed in Figure 6 emphasizes the robustness of the proposed framework in handling network instability and hardware variability, ensuring that slower participants do not become bottlenecks. The initial fluctuations in training accuracy during the early rounds, as highlighted in Figures 3 and 5, suggest a trade-off between aggregation frequency and model stability. Nonetheless, by round 30, the convergence behavior of our framework aligns closely with HierFAVG, with a final accuracy difference of only 0.4%, indicating that asynchronous aggregation does not compromise the overall quality of the model. This is crucial in IoT environments, where timely and efficient updates are often prioritized over synchronized operations. The analysis of encryption, as presented in Table 3, illustrates that the encryption overhead is minimal, averaging 0.8 s per round, and thus suitable for federated-learning systems involving resource-constrained IoT devices. The results demonstrate that our framework not only maintains data security but also operates within the practical computational limits of IoT devices. Our framework's adaptability and performance suggest practical applications across a wide range of IoT scenarios, particularly those involving heterogeneous devices and dynamic environments. For instance, the HAR dataset used in our experiments exemplifies human activity recognition systems where data are collected from wearable devices via embedded sensors. These systems are inherently heterogeneous, with devices varying in computational capabilities and connectivity. Our framework's asynchronous aggregation mechanism ensures that updates from such diverse devices are efficiently handled, maintaining timely and accurate predictions even under network instability. In healthcare monitoring, this advantage translates directly to secure and efficient communication of sensitive patient data from wearable devices to cloud servers, leveraging our lightweight encryption to protect privacy without overburdening resource-constrained devices. Similarly, in broader IoT applications such as industrial IoT and smart agriculture, the framework's scalability enables it to manage data aggregation from numerous sensors monitoring machinery, environmental conditions, or crop health, optimizing communication overhead and ensuring robust system responsiveness.