# Federated Reinforcement Learning in IoT: Applications, Opportunities and Open Challenges
## 3 Methodology
### FRL Applications in the IoT in Terms of Security
The author in [20] proposes a federated RL-based clinical decision system for edge computing environments with resource constraints and privacy preservation concerns. The system uses multiple agents to collaborate and utilize each other's experiences to make clinical decisions collaboratively and optimizes decision-making policies using reinforcement learning. The authors proposed a new algorithm called double deep Q-network (DDQN) within a fully decentralized federated framework (FDFF) made possible by an integrated system known as SMEC. This algorithm ensures the privacy and security of patient data and detects malicious behavior by providing a reliable way to develop a treatment policy in real-time using multiple distributed electronic medical records (EMRs). To ensure the confidentiality of EMRs, additively homomorphic encryption is employed. Security issues related to traffic monitoring in SDN-based IoT networks are a big problem. To overcome these issues, [PERSON] et al. [101] suggest a method for monitoring IoT network traffic based on SDN using FDRL. The proposed approach utilizes a control algorithm based on double deep Q-network (DDQN) and a flow-rule matching field to supervise a specific IoT edge, facilitating effective traffic monitoring. The authors suggest utilizing federated RL to enhance the learning version of the DDQN algorithm mentioned earlier and enable the SDN controllers to collaborate and make traffic monitoring decisions based on shared data. Their results demonstrate that the system can allow SDN controllers to monitor network traffic and make informed decisions efficiently. The study found that using a federated DDQN approach can decrease learning loss and learning cycles by 66% and 40%, compared to the traditional approach, in situations with high granularity requirements. Additionally, the deep monitor framework improved the IDS application's attack detection performance by 22.83% compared to the FlowStat solution. In reference [86], the authors suggest a method called FRL for automatically controlling software-defined networking (SDN)-based IoT systems while prioritizing data security. This approach allows IoT devices to independently learn and adjust to the network's changing conditions without needing a central controller. The experiments show that the proposed approach can effectively optimize network performance while reducing the computational burden on individual devices. They utilized the actor-critic PPO, which is a kind of reinforcement learning algorithm. They also introduced two federation policies--transfer learning and gradient sharing--to enhance learning speed and overall performance. Wireless networks are vulnerable to security breaches and intrusions due to the lack of clear boundaries. As cyber intruders continue to grow, the risk of compromising critical applications monitored by networked systems has also increased. A federated reinforcement learning-based intrusion detection system (FRL-IDS) has been proposed in [91], for healthcare infrastructures in the IoT networks to address these security concerns. The system is designed to manage the challenge of detecting attacks in IoT-enabled healthcare systems, which are increasingly vulnerable to security threats due to many related devices and the sensitive character of the data they control. FRL allows multiple IDSs to collaborate and learn from each other without sharing sensitive data, thereby addressing the privacy issue in healthcare systems. The suggested system comprises a central server that manages the training of intrusion detection system (IDS) models using federated reinforcement learning (FRL), along with several edge devices that operate local IDSs. The edge devices train their IDSs on local data and periodically upload the trained models to the central server, aggregating them to enhance the system's overall performance. Current research on IIoT routing primarily concentrates on latency and routing reliability but often overlooks the importance of privacy and security in the routing process. [PERSON] et al.