# FL-MD3 QN-Based IoT Intelligent Access Algorithm for Smart Construction Sites
## 1 Introduction  
With the deployment of fifth-generation (5G) mobile communication technology and rapid advancements in cutting-edge technologies like artificial intelligence and edge computing, smart construction sites have become pivotal for the construction industry's transformation. These sites integrate Internet of Things (IoT), big data, and other technologies to achieve intelligent, automated, and visual management [1, 2, 3]. IoT serves as the backbone for data collection and transmission in smart construction sites. It uses sensors and devicesto gather real-time environmental, equipment, and personnel data, transmitting them to management systems for decision making [4; 5]. The IoT system in smart construction sites includes diverse sensors and devices, such as environmental monitors, video surveillance equipment, smart access controls, and personnel trackers. These generate multi-modal data (text, audio, images, and video), requiring high bandwidth, speed, low latency, and security [6; 7]. Dynamic Spectrum Access (DSA) addresses bandwidth optimization challenges [8; 9]. To improve DSA efficiency, various techniques have been proposed. Reference [10] developed a channel state prediction model and a spectrum access strategy for secondary users using Markov decision processes. Ref. [11] introduced a stochastic channel access strategy with dual-channel spectrum sensing to enhance secondary user (SU) sensing and reduce channel collisions. Ref. [12] designed a low-complexity learning algorithm for multi-user, multi-channel cognitive radio networks. Ref. [13] used neural networks for dynamic relay selection in cognitive radio (CR) networks. While these models improve spectrum efficiency, they struggle in complex spectrum environments. While the aforementioned models have improved spectral efficiency, they have not addressed the multi-objective optimization problem in complex spectrum environments, thus failing to meet the access requirements in such environments. Ref. [14] applied Q-learning to DSA, enabling SUs to update Q-functions from environmental feedback, reducing convergence iterations. Ref. [15] combined reinforcement learning (RL) and Bayesian algorithms to predict channel idle time, boosting throughput by minimizing sensing overhead. Ref. [16] deployed a Deep Q-Network (DQN) model in distributed DSA to enhance spectrum access. Ref. [17] optimized DQN for long-term secondary throughput, proposing an adjusted deep deterministic policy gradient. Traditional DQN suffers from overestimation bias due to greedy Q-value computation, limiting multi-objective optimization. Double Deep Q-Network (DDQN) [18] exhibits slow convergence in complex environments. D3 QN [19], combining Double DQN and Dueling DQN, mitigates Q-value overestimation and improves policy accuracy. Dueling DQN separates Q-values into state-value and action-advantage functions, enabling independent environmental state and action evaluation, ideal for long-term optimization in smart construction scenarios. FL ensures user privacy in wireless networks [20; 21]. Ref. [22] proposed a hierarchical FL framework for heterogeneous cellular networks, reducing communication latency via resource allocation. Ref. [23] introduced a client-edge-cloud FL system to lower user-cloud communication costs. Ref. [24] leveraged personalized FL and edge computing to address heterogeneity, achieving low-latency IoT processing. Ref. [25] developed a Federated Deep Reinforcement Learning framework for DSA, safeguarding IoT user privacy. However, the aforementioned literature has not been designed for the multi-site collaborative optimization scenario in smart construction sites.