# FL-MD3 QN-Based IoT Intelligent Access Algorithm for Smart Construction Sites
## 4 Simulation and Analysis
### Simulation Result Analysis  
As shown in Table 5, in the dynamic IoT access scenarios of smart construction sites, significant differences exist in computational complexity and performance across algorithms. DQN, as the baseline algorithm, employs a single Q-network to map high-dimensional states to actions, resulting in a complexity of \(O(d^{2})\). Its training time reaches 143 min due to iterative corrections for Q-value overestimation caused by greedy policies. MD3 QN reduces parameters by 15% through sharing base layers in Dueling DQN and Double DQN, lowering complexity to \(O(0.85d^{2})\) and training time to 135 min. However, it lacks federated learning, failing to leverage multi-site data synergy. FL-DQN and FL-DDQN adopt distributed training to reduce single-node redundancy but retain structural flaws. FL-DQN maintains dual networks (local and global), increasing complexity to \(O(1.5d^{2})\). FL-DDQN further introduces target network updates, doubling parameters and synchronization frequency, leading to marginally longer training time (119 vs. 118 min). Both exhibit high federated communication overhead (FL-DQN: \"High\"; FL-DDQN: \"Relatively High\") due to uncompressed parameters, where communication delays account for 15-20% of training time, conflicting with IoT low-latency goals. In contrast, FL-MD3 QN achieves superior performance through three optimizations:  
Structural Compression: combines Dueling DQN's shared layers with pruning and quantization, reducing parameters by 30% and complexity to \(O(0.72d^{2})\). Efficient Federated Learning: implements dynamic aggregation and lightweight model compression (30% communication load reduction) with differential privacy (15% fewer communication rounds). \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Setting** \\ \hline \(\varepsilon\) & 0.3 \\ Optimizer & Adam \\ Multi-modal Service Distribution & Video 30\%, Text 20\%, Audio 10\%, Image 40\% \\ User Count & 300 \\ Construction Site Count & 20 \\ Initial Experience Pool Size & 50,000 \\ Priority Sampling Coefficient & 0.6 \\ Target Network Update Period & 200 \\ FL Noise Scale & 0.01 \\ Data Encryption Rate & 100\% \\ Data Leakage Risk Threshold & 0.01\% \\ Air Interface Latency & 5 ms \\ Mobility Support & High Speed \\ Bandwidth & 20 MHz \\ Operating Frequency Band & 2.6 GHz \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._ \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Complexity** & **Federated Communication Overhead** & **Training Time (min)** \\ \hline DQN [21] & \(O(d^{2})\) & None & 143 \\ MD3 QN & \(O(0.85d^{2})\) & None & 135 \\ FL-DQN [22] & \(O(1.5d^{2})\) & High & 118 \\ FL-MD3 QN & \(O(0.72d^{2})\) & Low & 110 \\ FL-DDQN [20] & \(O(1.5d^{2})\) & Relatively High & 119 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of complexity, federated communication overhead, and training time across different algorithms. Prioritized Experience Replay: filters high-value samples, reducing 20% redundant computation. These innovations cut training time to 110 min (23% faster than DQN). As shown in Table 6, the FL-MD3 QN algorithm demonstrates significant low-latency advantages in IoT access scenarios for smart construction sites. For diverse service types (text, audio, image, and video), FL-MD3 QN achieves not only lower average latency than other algorithms but also significantly smaller standard deviations (text: 0.03; video: 0.20), indicating high robustness across heterogeneous site environments. For text services, FL-MD3 QN reduces latency to 0.14 \(\pm\) 0.03 s, an 82.5% improvement over DQN (0.80 \(\pm\) 0.12 s), with a 75% reduction in standard deviation. This highlights FL-MD3 QN's ability to dynamically aggregate multi-site data via federated learning, rapidly adapting to low-bandwidth demands and mitigating latency fluctuations caused by local channel contention. For audio, image, and video services, FL-MD3 QN reduces latency by approximately 50-70% compared to baseline algorithms.