# Federated Reinforcement Learning in IoT: Applications, Opportunities and Open Challenges
## 3 Methodology
### FRL Applications in the IoT in Terms of Security  
The authors of [94] propose a novel approach for dynamic spectrum access (DSA) in the context of the IoT using federated deep reinforcement learning (FDRL). They present a set of techniques to enhance the efficiency of dynamic spectrum access in IoT environments. The complete system can be categorized into three stages: the first involves training a local double deep Q-learning network (DDQN) model, the second consists of aggregating the central model, and the third involves releasing the model parameters. Their findings indicate that the reinforcement learning algorithm has a significantly higher success rate than random channel access, particularly after several iterations. Incorporating FL into the approach results in even faster convergence. The proposed scheme aims to address the security of terminal data and personal privacy data in a context where multiple devices communicate with each other. Additionally, it maintains IoT users' confidentiality since the FDRL method only requires the uploading of model parameters to edge servers. In reference [95], the authors propose a framework that merges blockchain and federated learning to improve the protection and confidentiality of acquired model characteristics. The structure comprises three components: local training, blockchain for parameter verification, and global aggregation. The edge servers maintain the blockchain. The deep reinforcement learning (DRL) method they use has three crucial elements: the main network, the target network, and replay memory. The primary and target networks each have two deep neural networks (DNNs): the actor DNN that links system states to actions, and the critic DNN that evaluates the effectiveness of policies and directs actions toward policy gradient direction. The objective network has a comparable arrangement to the primary network, and it generates desired outputs that help train the main critic DNN. The proposed approach suggests using blockchain to preserve learning parameters and validate their accuracy, which can improve the security and quality of the learning process. The authors of [88], similar to [95], present a new framework for channel resource allocation in 5G/B5G networks using federated reinforcement learning (FRL). They suggest utilizing FRL to enhance incumbent technologies' security in beyond 5G networks. The authors suggest using the FRL model for faster learning convergence and combining RL and FL methods in the proposed framework. The framework involves both local and global learning phases. The results of the study show that the FRL framework assembles much more quickly than the standard RL approach, indicating a significant performance improvement. The experimental results confirm that the presented technique improves the ability of WiFi networks in regard to throughput by selecting the optimal channel access parameters through collaborative learning. In reference [96], the authors introduce a decision-making system called Devote, which presents a solution to address security challenges in fog-based IoT environments. Devote utilizes a great algorithm to prioritize data services according to their importance while assuming the accessibility of resources at the fog node (FN). To deal with the playful nature of the IoT domain, the authors process IoT data in a way to adapt to changing conditions over time. In addition, they present a technique based on an online secretary approach for selecting the appropriate candidate FN for data offloading. The results demonstrate that Devote achieves lower service delay than other systems and a user satisfaction rate of 88.4%. The authors of [97] present a new approach to introduce a solution to address security concerns regarding privacy-preserving offloading in IoT environments enabled by the cloud. The proposed method combines the concepts of context awareness and privacy preservation to make offloading decisions more securely and efficiently.