## 5 Simulation
### Experimental Results and Analysis
#### 5.2.5 Scalability Evaluation  
Scalability is a critical aspect of IoT frameworks, particularly in large-scale deployments where efficient communication and computation are essential. Figure 6(a),b describe the scalability evaluation of our method compared to HierFAVG by analyzing average round time and throughput across varying numbers of IoT devices, including 4, 8, 12, 16, and 20. Figure 6(a) illustrates the comparison of average round time. Our framework maintains a near-constant trend in round time as the number of devices increases, reflecting the  
Figure 6: Round-time cost comparison between frameworks. (**a**) Round time cost for individual devices. (**b**) Total round-time cost over 50 rounds. Figure 7: Scalability evaluation. (**a**) Average round time across different numbers of IoT devices. (**b**) Throughput across different numbers of IoT devices. effectiveness of its asynchronous aggregation and sliding window mechanism. Specifically, the round time increases only modestly from 8.64 s (4 devices) to 10.71 s (20 devices), with fluctuations remaining minimal (standard deviation: 0.83 s). This stability arises from our asynchronous aggregation strategy, which decouples the global model update process from device synchronization. Unlike traditional synchronous aggregation approaches, where the slowest devices (stragglers) delay the entire process, our method allows updates to proceed without waiting for these outliers, therefore significantly reducing round-time variability. Moreover, our sliding window mechanism dynamically adjusts the aggregation interval based on device participation rates, ensuring that devices with lower computational power or unstable connectivity do not unduly impact the system's overall performance. These mechanisms collectively enable our framework to achieve a stable and scalable performance profile as the number of devices grows. Conversely, although HierFAVG also employs edge-level aggregation, its synchronous nature inherently amplifies delays caused by stragglers, leading to a linear increase in round time with the addition of more devices. Figure 7b presents the throughput performance. Our framework demonstrates a consistent upward trend in throughput as the number of devices increases, emphasizing its capability to effectively handle larger-scale IoT deployments. The throughput grows from 403.64 KB/s (4 devices) to 1605.04 KB/s (20 devices), achieving a 298.0% improvement. In contrast, HierFAVG also exhibits an increasing trend in throughput, growing from 338.18 KB/s (4 devices) to 1205.29 KB/s (20 devices), representing a 256.4% improvement. However, the synchronous nature of HierFAVG introduces significant delays as the number of devices increases, primarily due to the need to wait for all devices to complete their updates before aggregation. This synchronization overhead becomes a bottleneck in larger networks, limiting its ability to fully utilize available resources. The observed divergence between the two methods becomes more pronounced as the network scales. For instance, with 4 devices, our framework achieves a 19.4% higher throughput compared to HierFAVG (403.64 KB/s vs. 338.18 KB/s). This advantage expands to 33.2% with 20 devices (1605.04 KB/s vs. 1205.29 KB/s), reflecting the increasing impact of asynchronous aggregation in mitigating the challenges of device heterogeneity and communication delays. From a scalability perspective, these trends indicate that our framework is well-equipped to handle even larger IoT networks. The hierarchical and adaptive design, combined with efficient aggregation mechanisms, suggests that throughput will continue to scale linearly or sub-linearly with additional devices as long as the edge nodes maintain sufficient computational and communication capacity. Furthermore, the diminishing marginal increase in round time observed in Figure 7a implies that our framework effectively balances computational and communication workloads, avoiding the bottlenecks that hinder synchronous methods like HierFAVG.